diff --git a/.flake8 b/.flake8
new file mode 100644
index 0000000000000000000000000000000000000000..57d083009957dd371fb67cee54b80d356b10fe7e
--- /dev/null
+++ b/.flake8
@@ -0,0 +1,3 @@
+[flake8]
+max-line-length = 100
+ignore = E203 E741 W503 E731
diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..9141c54ac6d024b319eb9c68a3983a497dc46bf1
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,6 @@
+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
new file mode 100644
index 0000000000000000000000000000000000000000..3c6ea656430ce2fbfd061bb6bbb361b7766bd181
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -0,0 +1,81 @@
+# Copyright (c) Delanoe Pirard / Aedelon
+# Licensed under the Apache License, Version 2.0
+
+name: Bug Report
+description: Report a bug in awesome-depth-anything-3
+labels: ["bug"]
+body:
+ - type: markdown
+ attributes:
+ value: |
+ Thanks for reporting! Please fill out the form below.
+
+ **Note**: For issues with the model itself (accuracy, artifacts), please report to the [upstream repository](https://github.com/ByteDance-Seed/Depth-Anything-3/issues).
+
+ - type: textarea
+ id: description
+ attributes:
+ label: Bug Description
+ description: What happened? What did you expect to happen?
+ placeholder: Describe the bug...
+ validations:
+ required: true
+
+ - type: textarea
+ id: reproduction
+ attributes:
+ label: Steps to Reproduce
+ description: Minimal code or steps to reproduce the issue
+ placeholder: |
+ ```python
+ from depth_anything_3.api import DepthAnything3
+ model = DepthAnything3.from_pretrained("depth-anything/DA3-LARGE")
+ # ...
+ ```
+ validations:
+ required: true
+
+ - type: textarea
+ id: traceback
+ attributes:
+ label: Error Traceback
+ description: Full error message and traceback
+ render: shell
+ placeholder: Paste the full traceback here...
+
+ - type: input
+ id: version
+ attributes:
+ label: Package Version
+ placeholder: "0.1.0"
+ validations:
+ required: true
+
+ - type: dropdown
+ id: device
+ attributes:
+ label: Device
+ options:
+ - CUDA (NVIDIA GPU)
+ - MPS (Apple Silicon)
+ - CPU
+ validations:
+ required: true
+
+ - type: input
+ id: pytorch
+ attributes:
+ label: PyTorch Version
+ placeholder: "2.9.0"
+
+ - type: input
+ id: python
+ attributes:
+ label: Python Version
+ placeholder: "3.11"
+
+ - type: input
+ id: os
+ attributes:
+ label: Operating System
+ placeholder: "macOS 14.0 / Ubuntu 22.04 / Windows 11"
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 0000000000000000000000000000000000000000..5172f095d218588d40274d6f22d07e98bf73fbd1
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,8 @@
+blank_issues_enabled: false
+contact_links:
+ - name: Upstream Repository
+ url: https://github.com/ByteDance-Seed/Depth-Anything-3/issues
+ about: For issues with the model architecture, accuracy, or training
+ - name: Discussions
+ url: https://github.com/Aedelon/awesome-depth-anything-3/discussions
+ about: Ask questions or share ideas
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
new file mode 100644
index 0000000000000000000000000000000000000000..0a01f833b87037c2b06561ab4270af51e4c26ec3
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -0,0 +1,61 @@
+# Copyright (c) Delanoe Pirard / Aedelon
+# Licensed under the Apache License, Version 2.0
+
+name: Feature Request
+description: Suggest a new feature or improvement
+labels: ["enhancement"]
+body:
+ - type: markdown
+ attributes:
+ value: |
+ Thanks for your suggestion!
+
+ **Note**: For model/architecture changes, please suggest to the [upstream repository](https://github.com/ByteDance-Seed/Depth-Anything-3/issues).
+ This fork focuses on optimization, deployment, and developer experience.
+
+ - type: textarea
+ id: problem
+ attributes:
+ label: Problem / Use Case
+ description: What problem does this solve? What are you trying to do?
+ placeholder: I'm trying to...
+ validations:
+ required: true
+
+ - type: textarea
+ id: solution
+ attributes:
+ label: Proposed Solution
+ description: How would you like it to work?
+ placeholder: It would be great if...
+ validations:
+ required: true
+
+ - type: textarea
+ id: alternatives
+ attributes:
+ label: Alternatives Considered
+ description: Any other approaches you've considered?
+ placeholder: I also thought about...
+
+ - type: dropdown
+ id: category
+ attributes:
+ label: Category
+ options:
+ - Performance optimization
+ - CLI improvement
+ - API enhancement
+ - Documentation
+ - Testing
+ - CI/CD
+ - Other
+ validations:
+ required: true
+
+ - type: checkboxes
+ id: contribution
+ attributes:
+ label: Contribution
+ options:
+ - label: I would be willing to submit a PR for this feature
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000000000000000000000000000000000000..5ca6b850026110a7bfc5d56b2495af6ab604f45d
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,71 @@
+# Copyright (c) Delanoe Pirard / Aedelon
+# Licensed under the Apache License, Version 2.0
+
+name: CI
+
+on:
+ push:
+ branches: [main]
+ pull_request:
+ branches: [main]
+
+jobs:
+ lint:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Install uv
+ uses: astral-sh/setup-uv@v4
+
+ - name: Set up Python
+ run: uv python install 3.11
+
+ - name: Install dependencies
+ run: uv sync --extra dev
+
+ - name: Lint with ruff
+ run: uv run ruff check src/
+
+ test:
+ runs-on: ubuntu-latest
+ strategy:
+ fail-fast: false
+ matrix:
+ python-version: ["3.10", "3.11", "3.12"]
+
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Install uv
+ uses: astral-sh/setup-uv@v4
+
+ - name: Set up Python ${{ matrix.python-version }}
+ run: uv python install ${{ matrix.python-version }}
+
+ - name: Install dependencies
+ run: uv sync --extra dev
+
+ - name: Run tests
+ run: uv run pytest tests/ -v --tb=short -x
+ env:
+ PYTORCH_ENABLE_MPS_FALLBACK: "1"
+
+ build:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+ with:
+ fetch-depth: 0 # Required for hatch-vcs
+
+ - name: Install uv
+ uses: astral-sh/setup-uv@v4
+
+ - name: Set up Python
+ run: uv python install 3.11
+
+ - name: Build package
+ run: uv build
+
+ - name: Check package
+ run: uvx twine check dist/*
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
new file mode 100644
index 0000000000000000000000000000000000000000..671c6a69ff13feec23776d104f37197553870155
--- /dev/null
+++ b/.github/workflows/publish.yml
@@ -0,0 +1,34 @@
+# Copyright (c) Delanoe Pirard / Aedelon
+# Licensed under the Apache License, Version 2.0
+
+name: Publish to PyPI
+
+on:
+ release:
+ types: [published]
+
+jobs:
+ publish:
+ runs-on: ubuntu-latest
+ environment: pypi
+ permissions:
+ id-token: write # Required for trusted publishing
+
+ steps:
+ - uses: actions/checkout@v4
+ with:
+ fetch-depth: 0 # Required for hatch-vcs version
+
+ - name: Install uv
+ uses: astral-sh/setup-uv@v4
+
+ - name: Set up Python
+ run: uv python install 3.11
+
+ - name: Build package
+ run: uv build
+
+ - name: Publish to PyPI
+ uses: pypa/gh-action-pypi-publish@release/v1
+ # Uses trusted publishing (OIDC) - no API token needed
+ # Configure at: https://pypi.org/manage/project/awesome-depth-anything-3/settings/publishing/
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..42557f1d41d01ef128e4a03e9d2353a3555289d3
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,63 @@
+# Python cache
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+
+# Distribution / packaging
+workspace/
+build/
+dist/
+*.egg-info/
+.eggs/
+*.egg
+
+# Virtual environments
+.venv/
+venv/
+ENV/
+
+# Test/coverage
+.coverage
+.pytest_cache/
+htmlcov/
+.tox/
+.nox/
+coverage.xml
+*.cover
+
+# Jupyter notebooks
+.ipynb_checkpoints/
+
+# IDE
+.vscode/
+.idea/
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# Project-specific
+gallery*/
+debug*/
+DA3HF*/
+gradio_workspace/
+eval_workspace/
+FILTER*/
+input_images*/
+*.gradio/
+.gradio/
+src/debug_main.py
+temp*.png
+/outputs
+
+# Model weights and large files
+*.pt
+*.pth
+*.ckpt
+*.safetensors
+!assets/**/*.pt
+
+# Logs
+*.log
+logs/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3d9935935aee777bc71316f2e999759f8e612df2
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,59 @@
+repos:
+ - repo: 'https://github.com/pre-commit/pre-commit-hooks'
+ rev: v4.5.0
+ hooks:
+ - id: check-added-large-files
+ args:
+ - '--maxkb=125'
+ - id: check-ast
+ - id: check-executables-have-shebangs
+ - id: check-merge-conflict
+ - id: check-symlinks
+ - id: check-toml
+ - id: check-yaml
+ - id: debug-statements
+ - id: detect-private-key
+ - id: end-of-file-fixer
+ - id: no-commit-to-branch
+ args:
+ - '--branch'
+ - 'master'
+ - id: pretty-format-json
+ exclude: '.*\.ipynb$'
+ args:
+ - '--autofix'
+ - '--indent'
+ - '4'
+ - id: trailing-whitespace
+ args:
+ - '--markdown-linebreak-ext=md'
+ - repo: 'https://github.com/pycqa/isort'
+ rev: 5.13.2
+ hooks:
+ - id: isort
+ args:
+ - '--settings-file'
+ - 'pyproject.toml'
+ - '--filter-files'
+ - repo: 'https://github.com/asottile/pyupgrade'
+ rev: v3.15.2
+ hooks:
+ - id: pyupgrade
+ args: [--py38-plus, --keep-runtime-typing]
+ - repo: 'https://github.com/psf/black.git'
+ rev: 24.3.0
+ hooks:
+ - id: black
+ args:
+ - '--config=pyproject.toml'
+ - repo: 'https://github.com/PyCQA/flake8'
+ rev: 7.0.0
+ hooks:
+ - id: flake8
+ args:
+ - '--config=.flake8'
+ - repo: 'https://github.com/myint/autoflake'
+ rev: v1.4
+ hooks:
+ - id: autoflake
+ args: [ '--remove-all-unused-imports', '--recursive', '--remove-unused-variables', '--in-place']
diff --git a/BENCHMARKS.md b/BENCHMARKS.md
new file mode 100644
index 0000000000000000000000000000000000000000..e48b06c6d33c40e68d214fbb53cf1ae0ba453394
--- /dev/null
+++ b/BENCHMARKS.md
@@ -0,0 +1,217 @@
+# Benchmark Results
+
+Performance benchmarks comparing **awesome-depth-anything-3** (optimized fork) against the vanilla upstream implementation.
+
+> **Test Environment**: Apple Silicon (M-series), PyTorch 2.9.0
+> **Models**: da3-small, da3-base, da3-large, da3-giant
+
+---
+
+## Quick Summary
+
+| Feature | Improvement |
+|---------|-------------|
+| Model Loading (cached) | **200x faster** (0.8s → 0.005s) |
+| Inference (MPS, batch 4) | **1.14x faster** |
+| Cold Load Time | **1.7x faster** |
+| Memory Efficiency | Adaptive batching prevents OOM |
+
+---
+
+## 1. Awesome vs Upstream Comparison
+
+Direct comparison between this optimized fork and the original upstream repository.
+
+### MPS (Apple Silicon GPU)
+
+| Batch Size | Upstream | Awesome | Speedup | Notes |
+|------------|----------|---------|---------|-------|
+| 1 | 3.47 img/s | 3.50 img/s | 1.01x | Minimal overhead |
+| 2 | 3.64 img/s | 3.83 img/s | 1.05x | Batching benefits |
+| **4** | 3.32 img/s | 3.78 img/s | **1.14x** | Best improvement |
+
+#### Model Loading Performance
+
+| Metric | Upstream | Awesome | Speedup |
+|--------|----------|---------|---------|
+| Cold Load | 1.28s | 0.77s | **1.7x** |
+| Cached Load | N/A | 0.005s | **~200x** |
+
+The model caching system is the standout feature - after the first load, subsequent loads are essentially instant.
+
+### CPU
+
+| Batch Size | Upstream | Awesome | Speedup |
+|------------|----------|---------|---------|
+| 1 | 0.27 img/s | 0.31 img/s | 1.13x |
+| 2 | 0.24 img/s | 0.24 img/s | 1.00x |
+| 4 | 0.17 img/s | 0.16 img/s | 0.95x |
+
+> **Note**: CPU performance is similar between versions since GPU-specific optimizations don't apply. The slight regression at batch 4 is within measurement noise.
+
+---
+
+## 2. Model Performance by Size
+
+Throughput benchmarks on MPS (Apple Silicon) with 1280x720 input images.
+
+| Model | Parameters | Batch 1 | Batch 4 | Best Config |
+|-------|------------|---------|---------|-------------|
+| **da3-small** | ~25M | 22.2 img/s | 27.2 img/s | B=4 SDPA |
+| **da3-base** | ~100M | 10.7 img/s | 11.6 img/s | B=4 SDPA |
+| **da3-large** | ~335M | 3.8 img/s | 3.8 img/s | B=1-2 |
+| **da3-giant** | ~1.1B | 1.6 img/s | 1.2 img/s | B=1 |
+
+### Latency (single image)
+
+| Model | MPS | CPU | MPS Speedup |
+|-------|-----|-----|-------------|
+| da3-small | 45 ms | ~3,500 ms | ~78x |
+| da3-base | 94 ms | ~7,000 ms | ~74x |
+| da3-large | 265 ms | ~3,900 ms | ~15x |
+| da3-giant | 618 ms | N/A | - |
+
+---
+
+## 3. Preprocessing Pipeline
+
+### Strategy: Hybrid CPU/GPU
+
+On Apple Silicon, **CPU preprocessing is faster** than GPU (Kornia) due to optimized OpenCV/Accelerate routines. The overhead of MPS kernel launches exceeds the benefit for image transforms.
+
+| Resolution | CPU Time | GPU Time | Winner |
+|------------|----------|----------|--------|
+| 640x480 | 6.0 ms | N/A | CPU |
+| 1920x1080 | 18.7 ms | N/A | CPU |
+| 3840x2160 | 57.0 ms | N/A | CPU |
+
+> **Design Decision**: GPU preprocessing is automatically disabled on MPS. The GPU is reserved for model inference where it provides 15-78x speedup.
+
+### CUDA (NVIDIA)
+
+On CUDA, GPU preprocessing with NVJPEG provides significant benefits for JPEG decoding directly to GPU memory, eliminating CPU→GPU transfer overhead.
+
+---
+
+## 4. Attention Mechanisms
+
+Comparison between SDPA (Scaled Dot-Product Attention / Flash Attention) and manual attention implementation.
+
+### Per-Layer Performance
+
+| Config | SDPA | Manual | Speedup |
+|--------|------|--------|---------|
+| ViT-L 518px (MPS) | 2.21 ms | 1.86 ms | 0.8x |
+| ViT-L 1024px (MPS) | 9.91 ms | 5.87 ms | 0.6x |
+| ViT-L 518px (CPU) | 3.75 ms | 4.96 ms | 1.3x |
+| ViT-L 1024px (CPU) | 11.73 ms | 16.85 ms | 1.4x |
+
+> **Insight**: On MPS, manual attention is faster for ViT due to MPS's SDPA implementation overhead. On CPU, SDPA benefits from optimized BLAS operations.
+
+### End-to-End Impact
+
+| Model | SDPA | Manual | Best |
+|-------|------|--------|------|
+| da3-small | 21.8 img/s | 22.2 img/s | Manual |
+| da3-base | 9.8 img/s | 10.7 img/s | Manual |
+| da3-large | 3.8 img/s | 3.7 img/s | SDPA |
+| da3-giant | 1.6 img/s | 1.6 img/s | Tie |
+
+---
+
+## 5. Adaptive Batching
+
+The adaptive batching system dynamically adjusts batch size based on available GPU memory.
+
+### Test: 20 images with da3-large on MPS
+
+| Strategy | Total Time | Throughput | Batches Used |
+|----------|------------|------------|--------------|
+| Fixed B=1 | 5,612 ms | 3.6 img/s | [1,1,1...] |
+| Fixed B=2 | 5,514 ms | **3.6 img/s** | [2,2,2...] |
+| Fixed B=4 | 8,305 ms | 2.4 img/s | [4,4,4,4,4] |
+| Adaptive 85% | 5,637 ms | 3.5 img/s | [4,4,4...] |
+
+> **Recommendation**: For MPS with da3-large, fixed batch size of 2 provides optimal throughput. Adaptive batching is more valuable for:
+> - Variable input sizes
+> - Unknown GPU memory constraints
+> - Preventing OOM errors on smaller GPUs
+
+---
+
+## 6. Cross-Device Comparison
+
+### Inference Throughput (da3-large, batch=1)
+
+```
+MPS (Apple Silicon) ████████████████████████████████████████ 3.7 img/s
+CPU ███ 0.3 img/s
+```
+
+**MPS provides ~12x speedup over CPU** for da3-large inference.
+
+### Attention Layer (ViT-L 518px, SDPA)
+
+```
+MPS ████████████████████████ 2.40 ms
+CPU ███████████████████████████████████████ 3.75 ms
+```
+
+---
+
+## 7. Optimization Recommendations
+
+### For Apple Silicon (MPS)
+
+1. **Use model caching** - 200x faster subsequent loads
+2. **Batch size 2-4** for da3-small/base, **batch 1-2** for da3-large/giant
+3. **Let CPU handle preprocessing** - it's faster than MPS for image transforms
+4. **SDPA vs Manual**: Both are similar; SDPA slightly better for larger models
+
+### For NVIDIA CUDA
+
+1. **Enable GPU preprocessing** with NVJPEG for JPEG inputs
+2. **Use SDPA** (Flash Attention) - significant speedup
+3. **Larger batch sizes** benefit more from GPU parallelism
+4. **Adaptive batching** to maximize VRAM utilization
+
+### For CPU-only
+
+1. **Use smallest viable model** (da3-small: 22x faster than da3-giant)
+2. **Batch size 1** is optimal (memory bandwidth limited)
+3. **SDPA provides 1.3-1.4x speedup** on CPU
+
+---
+
+## Running Benchmarks
+
+```bash
+# Quick benchmark (fewer iterations)
+uv run python benchmarks/full_benchmark.py --quick
+
+# Full benchmark on specific device
+uv run python benchmarks/full_benchmark.py --device mps
+uv run python benchmarks/full_benchmark.py --device cuda
+uv run python benchmarks/full_benchmark.py --device cpu
+
+# Compare against upstream (requires upstream repo)
+uv run python benchmarks/comparative_benchmark.py --device all
+
+# Skip specific tests
+uv run python benchmarks/full_benchmark.py --skip-batching
+```
+
+---
+
+## Methodology
+
+- **Warmup**: 2 inference passes before timing
+- **Runs**: 3-5 iterations per configuration
+- **Synchronization**: `torch.mps.synchronize()` / `torch.cuda.synchronize()` for accurate GPU timing
+- **Memory cleanup**: `gc.collect()` + cache clearing between tests
+- **Input**: Synthetic 1280x720 RGB images (consistent across tests)
+
+---
+
+*Benchmarks last updated: December 2024*
+*Hardware: Apple Silicon (M-series) | Software: PyTorch 2.9.0*
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 0000000000000000000000000000000000000000..7e6f39f27d7e68ae66d5cb73f4967cd90938155c
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,36 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [Unreleased]
+
+## [0.1.0] - 2024-12-03
+
+### Added
+
+- **Model Caching**: ~200x faster model loading after first use via `ModelCache` singleton
+- **Adaptive Batching**: Automatic batch size optimization based on available GPU memory
+ - `batch_inference()` method with `batch_size="auto"` option
+ - `get_optimal_batch_size()` for memory-aware batch sizing
+- **CLI Batching Options**: `--batch-size`, `--max-batch-size`, `--target-memory-utilization`
+- **Apple Silicon Optimizations**: Smart CPU/GPU preprocessing selection for MPS
+- **GPU Preprocessing**: Kornia-based GPU preprocessing with NVJPEG support on CUDA
+- **Comprehensive Benchmarks**: Performance comparison scripts and documentation
+- **PyPI Package**: Published as `awesome-depth-anything-3`
+- **CI/CD**: GitHub Actions for testing, linting, and PyPI publishing
+- **HF Spaces Demo**: Interactive Gradio demo on Hugging Face
+- **Colab Tutorial**: Interactive notebook with examples
+
+### Changed
+
+- Package renamed from `depth-anything-3` to `awesome-depth-anything-3`
+- Improved error handling in CLI commands
+- Better logging with configurable levels
+
+### Credits
+
+This package is an optimized fork of [Depth Anything 3](https://github.com/ByteDance-Seed/Depth-Anything-3)
+by ByteDance. All model architecture and weights are their work. See README for full attribution.
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000000000000000000000000000000000000..78eec8dd04e8b61bfc8f707633808268f4f93412
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,114 @@
+# Contributing to awesome-depth-anything-3
+
+Thank you for your interest in contributing! This document provides guidelines for contributing to this project.
+
+## Important Note
+
+This is an **optimized fork** of [Depth Anything 3](https://github.com/ByteDance-Seed/Depth-Anything-3) by ByteDance.
+
+- **Model/architecture changes** should be proposed to the [upstream repository](https://github.com/ByteDance-Seed/Depth-Anything-3)
+- **Optimization/deployment improvements** are welcome here
+
+## Development Setup
+
+```bash
+# Clone the repository
+git clone https://github.com/Aedelon/awesome-depth-anything-3.git
+cd awesome-depth-anything-3
+
+# Install with development dependencies (using uv)
+uv sync --extra dev
+
+# Or with pip
+pip install -e ".[dev]"
+```
+
+## Running Tests
+
+```bash
+# Run all tests
+uv run pytest tests/ -v
+
+# Run specific test file
+uv run pytest tests/test_adaptive_batching.py -v
+
+# Run with coverage
+uv run pytest tests/ --cov=src/depth_anything_3
+```
+
+## Code Style
+
+We use `ruff` for linting and formatting:
+
+```bash
+# Check for issues
+uv run ruff check src/
+
+# Auto-fix issues
+uv run ruff check src/ --fix
+
+# Format code
+uv run ruff format src/
+```
+
+## Pre-commit Hooks
+
+We recommend using pre-commit hooks:
+
+```bash
+uv run pre-commit install
+uv run pre-commit run --all-files
+```
+
+## Pull Request Process
+
+1. **Fork** the repository
+2. **Create a branch** for your feature (`git checkout -b feature/amazing-feature`)
+3. **Make your changes** with clear, descriptive commits
+4. **Run tests** and linting
+5. **Update documentation** if needed
+6. **Push** to your fork and **open a Pull Request**
+
+### PR Guidelines
+
+- Keep PRs focused on a single change
+- Include tests for new functionality
+- Update CHANGELOG.md for user-facing changes
+- Ensure CI passes before requesting review
+
+## Types of Contributions Welcome
+
+### Highly Welcome
+
+- Performance optimizations
+- Bug fixes
+- Documentation improvements
+- Test coverage improvements
+- CI/CD improvements
+- Device compatibility (CUDA, MPS, CPU)
+
+### Discuss First
+
+- New CLI commands
+- API changes
+- New dependencies
+
+### Redirect to Upstream
+
+- Model architecture changes
+- Training code changes
+- New model variants
+
+## Reporting Issues
+
+When reporting bugs, please include:
+
+- Python version
+- PyTorch version
+- Device type (CUDA/MPS/CPU)
+- Minimal reproduction code
+- Full error traceback
+
+## License
+
+By contributing, you agree that your contributions will be licensed under the Apache License 2.0.
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..a2fa36578f5094f56f4b0ec57e70eb30aced4519
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
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+ http://www.apache.org/licenses/
+
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+ 5. Submission of Contributions. Unless You explicitly state otherwise,
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+ 9. Accepting Warranty or Additional Liability. While redistributing
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+ on Your own behalf and on Your sole responsibility, not on behalf
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+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
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+ Copyright 2025 The Depth Anything 3 Team
+
+ Licensed under the Apache License, Version 2.0 (the "License");
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ac1a6caeeb7e923e4c754435ea49a984bf60843e
--- /dev/null
+++ b/README.md
@@ -0,0 +1,418 @@
+---
+title: Awesome Depth Anything 3
+emoji: 🌊
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 5.50.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Metric 3D reconstruction from images/video
+---
+
+
+
+This work presents **Depth Anything 3 (DA3)**, a model that predicts spatially consistent geometry from
+arbitrary visual inputs, with or without known camera poses.
+In pursuit of minimal modeling, DA3 yields two key insights:
+- 💎 A **single plain transformer** (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization,
+- ✨ A singular **depth-ray representation** obviates the need for complex multi-task learning.
+
+🏆 DA3 significantly outperforms
+[DA2](https://github.com/DepthAnything/Depth-Anything-V2) for monocular depth estimation,
+and [VGGT](https://github.com/facebookresearch/vggt) for multi-view depth estimation and pose estimation.
+All models are trained exclusively on **public academic datasets**.
+
+
+
+
+
+
+
+
+
+
+## 📰 News
+- **30-11-2025:** Add [`use_ray_pose`](#use-ray-pose) and [`ref_view_strategy`](docs/funcs/ref_view_strategy.md) (reference view selection for multi-view inputs).
+- **25-11-2025:** Add [Awesome DA3 Projects](#-awesome-da3-projects), a community-driven section featuring DA3-based applications.
+- **14-11-2025:** Paper, project page, code and models are all released.
+
+## ✨ Highlights
+
+### 🏆 Model Zoo
+We release three series of models, each tailored for specific use cases in visual geometry.
+
+- 🌟 **DA3 Main Series** (`DA3-Giant`, `DA3-Large`, `DA3-Base`, `DA3-Small`) These are our flagship foundation models, trained with a unified depth-ray representation. By varying the input configuration, a single model can perform a wide range of tasks:
+ + 🌊 **Monocular Depth Estimation**: Predicts a depth map from a single RGB image.
+ + 🌊 **Multi-View Depth Estimation**: Generates consistent depth maps from multiple images for high-quality fusion.
+ + 🎯 **Pose-Conditioned Depth Estimation**: Achieves superior depth consistency when camera poses are provided as input.
+ + 📷 **Camera Pose Estimation**: Estimates camera extrinsics and intrinsics from one or more images.
+ + 🟡 **3D Gaussian Estimation**: Directly predicts 3D Gaussians, enabling high-fidelity novel view synthesis.
+
+- 📐 **DA3 Metric Series** (`DA3Metric-Large`) A specialized model fine-tuned for metric depth estimation in monocular settings, ideal for applications requiring real-world scale.
+
+- 🔍 **DA3 Monocular Series** (`DA3Mono-Large`). A dedicated model for high-quality relative monocular depth estimation. Unlike disparity-based models (e.g., [Depth Anything 2](https://github.com/DepthAnything/Depth-Anything-V2)), it directly predicts depth, resulting in superior geometric accuracy.
+
+🔗 Leveraging these available models, we developed a **nested series** (`DA3Nested-Giant-Large`). This series combines a any-view giant model with a metric model to reconstruct visual geometry at a real-world metric scale.
+
+### 🛠️ Codebase Features
+Our repository is designed to be a powerful and user-friendly toolkit for both practical application and future research.
+- 🎨 **Interactive Web UI & Gallery**: Visualize model outputs and compare results with an easy-to-use Gradio-based web interface.
+- ⚡ **Flexible Command-Line Interface (CLI)**: Powerful and scriptable CLI for batch processing and integration into custom workflows.
+- 💾 **Multiple Export Formats**: Save your results in various formats, including `glb`, `npz`, depth images, `ply`, 3DGS videos, etc, to seamlessly connect with other tools.
+- 🔧 **Extensible and Modular Design**: The codebase is structured to facilitate future research and the integration of new models or functionalities.
+
+
+
+
+
+## 🚀 Quick Start
+
+### 📦 Installation
+
+```bash
+# From PyPI (recommended)
+pip install awesome-depth-anything-3
+
+# With Gradio web UI
+pip install awesome-depth-anything-3[app]
+
+# With CUDA optimizations (xformers + gsplat)
+pip install awesome-depth-anything-3[cuda]
+
+# Everything
+pip install awesome-depth-anything-3[all]
+```
+
+
+Development installation
+
+```bash
+git clone https://github.com/Aedelon/awesome-depth-anything-3.git
+cd awesome-depth-anything-3
+pip install -e ".[dev]"
+
+# Optional: 3D Gaussian Splatting head
+pip install --no-build-isolation git+https://github.com/nerfstudio-project/gsplat.git@0b4dddf
+```
+
+
+For detailed model information, please refer to the [Model Cards](#-model-cards) section below.
+
+### 💻 Basic Usage
+
+```python
+import glob, os, torch
+from depth_anything_3.api import DepthAnything3
+device = torch.device("cuda")
+model = DepthAnything3.from_pretrained("depth-anything/DA3NESTED-GIANT-LARGE")
+model = model.to(device=device)
+example_path = "assets/examples/SOH"
+images = sorted(glob.glob(os.path.join(example_path, "*.png")))
+prediction = model.inference(
+ images,
+)
+# prediction.processed_images : [N, H, W, 3] uint8 array
+print(prediction.processed_images.shape)
+# prediction.depth : [N, H, W] float32 array
+print(prediction.depth.shape)
+# prediction.conf : [N, H, W] float32 array
+print(prediction.conf.shape)
+# prediction.extrinsics : [N, 3, 4] float32 array # opencv w2c or colmap format
+print(prediction.extrinsics.shape)
+# prediction.intrinsics : [N, 3, 3] float32 array
+print(prediction.intrinsics.shape)
+```
+
+```bash
+
+export MODEL_DIR=depth-anything/DA3NESTED-GIANT-LARGE
+# This can be a Hugging Face repository or a local directory
+# If you encounter network issues, consider using the following mirror: export HF_ENDPOINT=https://hf-mirror.com
+# Alternatively, you can download the model directly from Hugging Face
+export GALLERY_DIR=workspace/gallery
+mkdir -p $GALLERY_DIR
+
+# CLI auto mode with backend reuse
+da3 backend --model-dir ${MODEL_DIR} --gallery-dir ${GALLERY_DIR} # Cache model to gpu
+da3 auto assets/examples/SOH \
+ --export-format glb \
+ --export-dir ${GALLERY_DIR}/TEST_BACKEND/SOH \
+ --use-backend
+
+# CLI video processing with feature visualization
+da3 video assets/examples/robot_unitree.mp4 \
+ --fps 15 \
+ --use-backend \
+ --export-dir ${GALLERY_DIR}/TEST_BACKEND/robo \
+ --export-format glb-feat_vis \
+ --feat-vis-fps 15 \
+ --process-res-method lower_bound_resize \
+ --export-feat "11,21,31"
+
+# CLI auto mode without backend reuse
+da3 auto assets/examples/SOH \
+ --export-format glb \
+ --export-dir ${GALLERY_DIR}/TEST_CLI/SOH \
+ --model-dir ${MODEL_DIR}
+
+```
+
+The model architecture is defined in [`DepthAnything3Net`](src/depth_anything_3/model/da3.py), and specified with a Yaml config file located at [`src/depth_anything_3/configs`](src/depth_anything_3/configs). The input and output processing are handled by [`DepthAnything3`](src/depth_anything_3/api.py). To customize the model architecture, simply create a new config file (*e.g.*, `path/to/new/config`) as:
+
+```yaml
+__object__:
+ path: depth_anything_3.model.da3
+ name: DepthAnything3Net
+ args: as_params
+
+net:
+ __object__:
+ path: depth_anything_3.model.dinov2.dinov2
+ name: DinoV2
+ args: as_params
+
+ name: vitb
+ out_layers: [5, 7, 9, 11]
+ alt_start: 4
+ qknorm_start: 4
+ rope_start: 4
+ cat_token: True
+
+head:
+ __object__:
+ path: depth_anything_3.model.dualdpt
+ name: DualDPT
+ args: as_params
+
+ dim_in: &head_dim_in 1536
+ output_dim: 2
+ features: &head_features 128
+ out_channels: &head_out_channels [96, 192, 384, 768]
+```
+
+Then, the model can be created with the following code snippet.
+```python
+from depth_anything_3.cfg import create_object, load_config
+
+Model = create_object(load_config("path/to/new/config"))
+```
+
+
+
+## 📚 Useful Documentation
+
+- 🖥️ [Command Line Interface](docs/CLI.md)
+- 📑 [Python API](docs/API.md)
+
+
+## 🗂️ Model Cards
+
+Generally, you should observe that DA3-LARGE achieves comparable results to VGGT.
+
+The Nested series uses an Any-view model to estimate pose and depth, and a monocular metric depth estimator for scaling.
+
+| 🗃️ Model Name | 📏 Params | 📊 Rel. Depth | 📷 Pose Est. | 🧭 Pose Cond. | 🎨 GS | 📐 Met. Depth | ☁️ Sky Seg | 📄 License |
+|-------------------------------|-----------|---------------|--------------|---------------|-------|---------------|-----------|----------------|
+| **Nested** | | | | | | | | |
+| [DA3NESTED-GIANT-LARGE](https://huggingface.co/depth-anything/DA3NESTED-GIANT-LARGE) | 1.40B | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | CC BY-NC 4.0 |
+| **Any-view Model** | | | | | | | | |
+| [DA3-GIANT](https://huggingface.co/depth-anything/DA3-GIANT) | 1.15B | ✅ | ✅ | ✅ | ✅ | | | CC BY-NC 4.0 |
+| [DA3-LARGE](https://huggingface.co/depth-anything/DA3-LARGE) | 0.35B | ✅ | ✅ | ✅ | | | | CC BY-NC 4.0 |
+| [DA3-BASE](https://huggingface.co/depth-anything/DA3-BASE) | 0.12B | ✅ | ✅ | ✅ | | | | Apache 2.0 |
+| [DA3-SMALL](https://huggingface.co/depth-anything/DA3-SMALL) | 0.08B | ✅ | ✅ | ✅ | | | | Apache 2.0 |
+| | | | | | | | | |
+| **Monocular Metric Depth** | | | | | | | | |
+| [DA3METRIC-LARGE](https://huggingface.co/depth-anything/DA3METRIC-LARGE) | 0.35B | ✅ | | | | ✅ | ✅ | Apache 2.0 |
+| | | | | | | | | |
+| **Monocular Depth** | | | | | | | | |
+| [DA3MONO-LARGE](https://huggingface.co/depth-anything/DA3MONO-LARGE) | 0.35B | ✅ | | | | | ✅ | Apache 2.0 |
+
+
+## ⚡ Performance Benchmarks
+
+Inference throughput measured on Apple Silicon (MPS) with PyTorch 2.9.0. For detailed benchmarks, see [BENCHMARKS.md](BENCHMARKS.md).
+
+### Apple Silicon (MPS) - Batch Size 1
+
+| Model | Latency | Throughput |
+|-------|---------|------------|
+| DA3-Small | 46 ms | **22 img/s** |
+| DA3-Base | 93 ms | **11 img/s** |
+| DA3-Large | 265 ms | **3.8 img/s** |
+| DA3-Giant | 618 ms | **1.6 img/s** |
+
+### Cross-Device Comparison (DA3-Large)
+
+| Device | Throughput | vs CPU |
+|--------|------------|--------|
+| CPU | 0.3 img/s | 1.0x |
+| Apple Silicon (MPS) | 3.8 img/s | **13x** |
+| NVIDIA L4 (CUDA) | 10.3 img/s | **34x** |
+
+### Batch Processing
+
+```python
+from depth_anything_3.api import DepthAnything3
+
+model = DepthAnything3.from_pretrained("depth-anything/DA3-LARGE")
+
+# Adaptive batching (recommended for large image sets)
+results = model.batch_inference(
+ images=image_paths,
+ batch_size="auto", # Automatically selects optimal batch size
+ target_memory_utilization=0.85,
+)
+
+# Fixed batch size
+results = model.batch_inference(
+ images=image_paths,
+ batch_size=4,
+)
+```
+
+> See [BENCHMARKS.md](BENCHMARKS.md) for comprehensive benchmarks including preprocessing, attention mechanisms, and adaptive batching strategies.
+
+
+## ❓ FAQ
+
+- **Monocular Metric Depth**: To obtain metric depth in meters from `DA3METRIC-LARGE`, use `metric_depth = focal * net_output / 300.`, where `focal` is the focal length in pixels (typically the average of fx and fy from the camera intrinsic matrix K). Note that the output from `DA3NESTED-GIANT-LARGE` is already in meters.
+
+- **Ray Head (`use_ray_pose`)**: Our API and CLI support `use_ray_pose` arg, which means that the model will derive camera pose from ray head, which is generally slightly slower, but more accurate. Note that the default is `False` for faster inference speed.
+
+ AUC3 Results for DA3NESTED-GIANT-LARGE
+
+ | Model | HiRoom | ETH3D | DTU | 7Scenes | ScanNet++ |
+ |-------|------|-------|-----|---------|-----------|
+ | `ray_head` | 84.4 | 52.6 | 93.9 | 29.5 | 89.4 |
+ | `cam_head` | 80.3 | 48.4 | 94.1 | 28.5 | 85.0 |
+
+
+
+
+
+
+- **Older GPUs without XFormers support**: See [Issue #11](https://github.com/ByteDance-Seed/Depth-Anything-3/issues/11). Thanks to [@S-Mahoney](https://github.com/S-Mahoney) for the solution!
+
+
+## 🏢 Awesome DA3 Projects
+
+A community-curated list of Depth Anything 3 integrations across 3D tools, creative pipelines, robotics, and web/VR viewers, including but not limited to these. You are welcome to submit your DA3-based project via PR, and we will review and feature it if applicable.
+
+- [DA3-blender](https://github.com/xy-gao/DA3-blender): Blender addon for DA3-based 3D reconstruction from a set of images.
+
+- [ComfyUI-DepthAnythingV3](https://github.com/PozzettiAndrea/ComfyUI-DepthAnythingV3): ComfyUI nodes for Depth Anything 3, supporting single/multi-view and video-consistent depth with optional point‑cloud export.
+
+- [DA3-ROS2-Wrapper](https://github.com/GerdsenAI/GerdsenAI-Depth-Anything-3-ROS2-Wrapper): Real-time DA3 depth in ROS2 with multi-camera support.
+
+- [VideoDepthViewer3D](https://github.com/amariichi/VideoDepthViewer3D): Streaming videos with DA3 metric depth to a Three.js/WebXR 3D viewer for VR/stereo playback.
+
+
+## 📝 Credits
+
+### Original Authors
+
+This package is built on top of **Depth Anything 3**, created by the ByteDance Seed team:
+
+- [Haotong Lin](https://haotongl.github.io/), [Sili Chen](https://github.com/SiliChen321), [Jun Hao Liew](https://liewjunhao.github.io/), [Donny Y. Chen](https://donydchen.github.io), [Zhenyu Li](https://zhyever.github.io/), [Guang Shi](https://scholar.google.com/citations?user=MjXxWbUAAAAJ), [Jiashi Feng](https://scholar.google.com.sg/citations?user=Q8iay0gAAAAJ), [Bingyi Kang](https://bingykang.github.io/)
+
+All model weights, architecture, and core algorithms are their work. This fork only adds production optimizations and deployment tooling.
+
+### Fork Maintainer
+
+This optimized fork is maintained by [Delanoe Pirard (Aedelon)](https://github.com/Aedelon).
+
+Contributions:
+- Model caching system
+- Adaptive batching
+- Apple Silicon (MPS) optimizations
+- PyPI packaging and CI/CD
+- Comprehensive benchmarking
+
+### Citation
+
+If you use Depth Anything 3 in your research, please cite the original paper:
+
+```bibtex
+@article{depthanything3,
+ title={Depth Anything 3: Recovering the visual space from any views},
+ author={Haotong Lin and Sili Chen and Jun Hao Liew and Donny Y. Chen and Zhenyu Li and Guang Shi and Jiashi Feng and Bingyi Kang},
+ journal={arXiv preprint arXiv:2511.10647},
+ year={2025}
+}
+```
+
+If you specifically use features from this fork (caching, batching, MPS optimizations), you may additionally reference:
+
+```
+awesome-depth-anything-3: https://github.com/Aedelon/awesome-depth-anything-3
+```
diff --git a/README.md.original b/README.md.original
new file mode 100644
index 0000000000000000000000000000000000000000..bb10cca51618474a701ea402f9f074038e0cda78
--- /dev/null
+++ b/README.md.original
@@ -0,0 +1,405 @@
+
+
+This work presents **Depth Anything 3 (DA3)**, a model that predicts spatially consistent geometry from
+arbitrary visual inputs, with or without known camera poses.
+In pursuit of minimal modeling, DA3 yields two key insights:
+- 💎 A **single plain transformer** (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization,
+- ✨ A singular **depth-ray representation** obviates the need for complex multi-task learning.
+
+🏆 DA3 significantly outperforms
+[DA2](https://github.com/DepthAnything/Depth-Anything-V2) for monocular depth estimation,
+and [VGGT](https://github.com/facebookresearch/vggt) for multi-view depth estimation and pose estimation.
+All models are trained exclusively on **public academic datasets**.
+
+
+
+
+
+
+
+
+
+
+## 📰 News
+- **30-11-2025:** Add [`use_ray_pose`](#use-ray-pose) and [`ref_view_strategy`](docs/funcs/ref_view_strategy.md) (reference view selection for multi-view inputs).
+- **25-11-2025:** Add [Awesome DA3 Projects](#-awesome-da3-projects), a community-driven section featuring DA3-based applications.
+- **14-11-2025:** Paper, project page, code and models are all released.
+
+## ✨ Highlights
+
+### 🏆 Model Zoo
+We release three series of models, each tailored for specific use cases in visual geometry.
+
+- 🌟 **DA3 Main Series** (`DA3-Giant`, `DA3-Large`, `DA3-Base`, `DA3-Small`) These are our flagship foundation models, trained with a unified depth-ray representation. By varying the input configuration, a single model can perform a wide range of tasks:
+ + 🌊 **Monocular Depth Estimation**: Predicts a depth map from a single RGB image.
+ + 🌊 **Multi-View Depth Estimation**: Generates consistent depth maps from multiple images for high-quality fusion.
+ + 🎯 **Pose-Conditioned Depth Estimation**: Achieves superior depth consistency when camera poses are provided as input.
+ + 📷 **Camera Pose Estimation**: Estimates camera extrinsics and intrinsics from one or more images.
+ + 🟡 **3D Gaussian Estimation**: Directly predicts 3D Gaussians, enabling high-fidelity novel view synthesis.
+
+- 📐 **DA3 Metric Series** (`DA3Metric-Large`) A specialized model fine-tuned for metric depth estimation in monocular settings, ideal for applications requiring real-world scale.
+
+- 🔍 **DA3 Monocular Series** (`DA3Mono-Large`). A dedicated model for high-quality relative monocular depth estimation. Unlike disparity-based models (e.g., [Depth Anything 2](https://github.com/DepthAnything/Depth-Anything-V2)), it directly predicts depth, resulting in superior geometric accuracy.
+
+🔗 Leveraging these available models, we developed a **nested series** (`DA3Nested-Giant-Large`). This series combines a any-view giant model with a metric model to reconstruct visual geometry at a real-world metric scale.
+
+### 🛠️ Codebase Features
+Our repository is designed to be a powerful and user-friendly toolkit for both practical application and future research.
+- 🎨 **Interactive Web UI & Gallery**: Visualize model outputs and compare results with an easy-to-use Gradio-based web interface.
+- ⚡ **Flexible Command-Line Interface (CLI)**: Powerful and scriptable CLI for batch processing and integration into custom workflows.
+- 💾 **Multiple Export Formats**: Save your results in various formats, including `glb`, `npz`, depth images, `ply`, 3DGS videos, etc, to seamlessly connect with other tools.
+- 🔧 **Extensible and Modular Design**: The codebase is structured to facilitate future research and the integration of new models or functionalities.
+
+
+
+
+
+## 🚀 Quick Start
+
+### 📦 Installation
+
+```bash
+# From PyPI (recommended)
+pip install awesome-depth-anything-3
+
+# With Gradio web UI
+pip install awesome-depth-anything-3[app]
+
+# With CUDA optimizations (xformers + gsplat)
+pip install awesome-depth-anything-3[cuda]
+
+# Everything
+pip install awesome-depth-anything-3[all]
+```
+
+
+Development installation
+
+```bash
+git clone https://github.com/Aedelon/awesome-depth-anything-3.git
+cd awesome-depth-anything-3
+pip install -e ".[dev]"
+
+# Optional: 3D Gaussian Splatting head
+pip install --no-build-isolation git+https://github.com/nerfstudio-project/gsplat.git@0b4dddf
+```
+
+
+For detailed model information, please refer to the [Model Cards](#-model-cards) section below.
+
+### 💻 Basic Usage
+
+```python
+import glob, os, torch
+from depth_anything_3.api import DepthAnything3
+device = torch.device("cuda")
+model = DepthAnything3.from_pretrained("depth-anything/DA3NESTED-GIANT-LARGE")
+model = model.to(device=device)
+example_path = "assets/examples/SOH"
+images = sorted(glob.glob(os.path.join(example_path, "*.png")))
+prediction = model.inference(
+ images,
+)
+# prediction.processed_images : [N, H, W, 3] uint8 array
+print(prediction.processed_images.shape)
+# prediction.depth : [N, H, W] float32 array
+print(prediction.depth.shape)
+# prediction.conf : [N, H, W] float32 array
+print(prediction.conf.shape)
+# prediction.extrinsics : [N, 3, 4] float32 array # opencv w2c or colmap format
+print(prediction.extrinsics.shape)
+# prediction.intrinsics : [N, 3, 3] float32 array
+print(prediction.intrinsics.shape)
+```
+
+```bash
+
+export MODEL_DIR=depth-anything/DA3NESTED-GIANT-LARGE
+# This can be a Hugging Face repository or a local directory
+# If you encounter network issues, consider using the following mirror: export HF_ENDPOINT=https://hf-mirror.com
+# Alternatively, you can download the model directly from Hugging Face
+export GALLERY_DIR=workspace/gallery
+mkdir -p $GALLERY_DIR
+
+# CLI auto mode with backend reuse
+da3 backend --model-dir ${MODEL_DIR} --gallery-dir ${GALLERY_DIR} # Cache model to gpu
+da3 auto assets/examples/SOH \
+ --export-format glb \
+ --export-dir ${GALLERY_DIR}/TEST_BACKEND/SOH \
+ --use-backend
+
+# CLI video processing with feature visualization
+da3 video assets/examples/robot_unitree.mp4 \
+ --fps 15 \
+ --use-backend \
+ --export-dir ${GALLERY_DIR}/TEST_BACKEND/robo \
+ --export-format glb-feat_vis \
+ --feat-vis-fps 15 \
+ --process-res-method lower_bound_resize \
+ --export-feat "11,21,31"
+
+# CLI auto mode without backend reuse
+da3 auto assets/examples/SOH \
+ --export-format glb \
+ --export-dir ${GALLERY_DIR}/TEST_CLI/SOH \
+ --model-dir ${MODEL_DIR}
+
+```
+
+The model architecture is defined in [`DepthAnything3Net`](src/depth_anything_3/model/da3.py), and specified with a Yaml config file located at [`src/depth_anything_3/configs`](src/depth_anything_3/configs). The input and output processing are handled by [`DepthAnything3`](src/depth_anything_3/api.py). To customize the model architecture, simply create a new config file (*e.g.*, `path/to/new/config`) as:
+
+```yaml
+__object__:
+ path: depth_anything_3.model.da3
+ name: DepthAnything3Net
+ args: as_params
+
+net:
+ __object__:
+ path: depth_anything_3.model.dinov2.dinov2
+ name: DinoV2
+ args: as_params
+
+ name: vitb
+ out_layers: [5, 7, 9, 11]
+ alt_start: 4
+ qknorm_start: 4
+ rope_start: 4
+ cat_token: True
+
+head:
+ __object__:
+ path: depth_anything_3.model.dualdpt
+ name: DualDPT
+ args: as_params
+
+ dim_in: &head_dim_in 1536
+ output_dim: 2
+ features: &head_features 128
+ out_channels: &head_out_channels [96, 192, 384, 768]
+```
+
+Then, the model can be created with the following code snippet.
+```python
+from depth_anything_3.cfg import create_object, load_config
+
+Model = create_object(load_config("path/to/new/config"))
+```
+
+
+
+## 📚 Useful Documentation
+
+- 🖥️ [Command Line Interface](docs/CLI.md)
+- 📑 [Python API](docs/API.md)
+
+
+## 🗂️ Model Cards
+
+Generally, you should observe that DA3-LARGE achieves comparable results to VGGT.
+
+The Nested series uses an Any-view model to estimate pose and depth, and a monocular metric depth estimator for scaling.
+
+| 🗃️ Model Name | 📏 Params | 📊 Rel. Depth | 📷 Pose Est. | 🧭 Pose Cond. | 🎨 GS | 📐 Met. Depth | ☁️ Sky Seg | 📄 License |
+|-------------------------------|-----------|---------------|--------------|---------------|-------|---------------|-----------|----------------|
+| **Nested** | | | | | | | | |
+| [DA3NESTED-GIANT-LARGE](https://huggingface.co/depth-anything/DA3NESTED-GIANT-LARGE) | 1.40B | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | CC BY-NC 4.0 |
+| **Any-view Model** | | | | | | | | |
+| [DA3-GIANT](https://huggingface.co/depth-anything/DA3-GIANT) | 1.15B | ✅ | ✅ | ✅ | ✅ | | | CC BY-NC 4.0 |
+| [DA3-LARGE](https://huggingface.co/depth-anything/DA3-LARGE) | 0.35B | ✅ | ✅ | ✅ | | | | CC BY-NC 4.0 |
+| [DA3-BASE](https://huggingface.co/depth-anything/DA3-BASE) | 0.12B | ✅ | ✅ | ✅ | | | | Apache 2.0 |
+| [DA3-SMALL](https://huggingface.co/depth-anything/DA3-SMALL) | 0.08B | ✅ | ✅ | ✅ | | | | Apache 2.0 |
+| | | | | | | | | |
+| **Monocular Metric Depth** | | | | | | | | |
+| [DA3METRIC-LARGE](https://huggingface.co/depth-anything/DA3METRIC-LARGE) | 0.35B | ✅ | | | | ✅ | ✅ | Apache 2.0 |
+| | | | | | | | | |
+| **Monocular Depth** | | | | | | | | |
+| [DA3MONO-LARGE](https://huggingface.co/depth-anything/DA3MONO-LARGE) | 0.35B | ✅ | | | | | ✅ | Apache 2.0 |
+
+
+## ⚡ Performance Benchmarks
+
+Inference throughput measured on Apple Silicon (MPS) with PyTorch 2.9.0. For detailed benchmarks, see [BENCHMARKS.md](BENCHMARKS.md).
+
+### Apple Silicon (MPS) - Batch Size 1
+
+| Model | Latency | Throughput |
+|-------|---------|------------|
+| DA3-Small | 46 ms | **22 img/s** |
+| DA3-Base | 93 ms | **11 img/s** |
+| DA3-Large | 265 ms | **3.8 img/s** |
+| DA3-Giant | 618 ms | **1.6 img/s** |
+
+### Cross-Device Comparison (DA3-Large)
+
+| Device | Throughput | vs CPU |
+|--------|------------|--------|
+| CPU | 0.3 img/s | 1.0x |
+| Apple Silicon (MPS) | 3.8 img/s | **13x** |
+| NVIDIA L4 (CUDA) | 10.3 img/s | **34x** |
+
+### Batch Processing
+
+```python
+from depth_anything_3.api import DepthAnything3
+
+model = DepthAnything3.from_pretrained("depth-anything/DA3-LARGE")
+
+# Adaptive batching (recommended for large image sets)
+results = model.batch_inference(
+ images=image_paths,
+ batch_size="auto", # Automatically selects optimal batch size
+ target_memory_utilization=0.85,
+)
+
+# Fixed batch size
+results = model.batch_inference(
+ images=image_paths,
+ batch_size=4,
+)
+```
+
+> See [BENCHMARKS.md](BENCHMARKS.md) for comprehensive benchmarks including preprocessing, attention mechanisms, and adaptive batching strategies.
+
+
+## ❓ FAQ
+
+- **Monocular Metric Depth**: To obtain metric depth in meters from `DA3METRIC-LARGE`, use `metric_depth = focal * net_output / 300.`, where `focal` is the focal length in pixels (typically the average of fx and fy from the camera intrinsic matrix K). Note that the output from `DA3NESTED-GIANT-LARGE` is already in meters.
+
+- **Ray Head (`use_ray_pose`)**: Our API and CLI support `use_ray_pose` arg, which means that the model will derive camera pose from ray head, which is generally slightly slower, but more accurate. Note that the default is `False` for faster inference speed.
+
+ AUC3 Results for DA3NESTED-GIANT-LARGE
+
+ | Model | HiRoom | ETH3D | DTU | 7Scenes | ScanNet++ |
+ |-------|------|-------|-----|---------|-----------|
+ | `ray_head` | 84.4 | 52.6 | 93.9 | 29.5 | 89.4 |
+ | `cam_head` | 80.3 | 48.4 | 94.1 | 28.5 | 85.0 |
+
+
+
+
+
+
+- **Older GPUs without XFormers support**: See [Issue #11](https://github.com/ByteDance-Seed/Depth-Anything-3/issues/11). Thanks to [@S-Mahoney](https://github.com/S-Mahoney) for the solution!
+
+
+## 🏢 Awesome DA3 Projects
+
+A community-curated list of Depth Anything 3 integrations across 3D tools, creative pipelines, robotics, and web/VR viewers, including but not limited to these. You are welcome to submit your DA3-based project via PR, and we will review and feature it if applicable.
+
+- [DA3-blender](https://github.com/xy-gao/DA3-blender): Blender addon for DA3-based 3D reconstruction from a set of images.
+
+- [ComfyUI-DepthAnythingV3](https://github.com/PozzettiAndrea/ComfyUI-DepthAnythingV3): ComfyUI nodes for Depth Anything 3, supporting single/multi-view and video-consistent depth with optional point‑cloud export.
+
+- [DA3-ROS2-Wrapper](https://github.com/GerdsenAI/GerdsenAI-Depth-Anything-3-ROS2-Wrapper): Real-time DA3 depth in ROS2 with multi-camera support.
+
+- [VideoDepthViewer3D](https://github.com/amariichi/VideoDepthViewer3D): Streaming videos with DA3 metric depth to a Three.js/WebXR 3D viewer for VR/stereo playback.
+
+
+## 📝 Credits
+
+### Original Authors
+
+This package is built on top of **Depth Anything 3**, created by the ByteDance Seed team:
+
+- [Haotong Lin](https://haotongl.github.io/), [Sili Chen](https://github.com/SiliChen321), [Jun Hao Liew](https://liewjunhao.github.io/), [Donny Y. Chen](https://donydchen.github.io), [Zhenyu Li](https://zhyever.github.io/), [Guang Shi](https://scholar.google.com/citations?user=MjXxWbUAAAAJ), [Jiashi Feng](https://scholar.google.com.sg/citations?user=Q8iay0gAAAAJ), [Bingyi Kang](https://bingykang.github.io/)
+
+All model weights, architecture, and core algorithms are their work. This fork only adds production optimizations and deployment tooling.
+
+### Fork Maintainer
+
+This optimized fork is maintained by [Delanoe Pirard (Aedelon)](https://github.com/Aedelon).
+
+Contributions:
+- Model caching system
+- Adaptive batching
+- Apple Silicon (MPS) optimizations
+- PyPI packaging and CI/CD
+- Comprehensive benchmarking
+
+### Citation
+
+If you use Depth Anything 3 in your research, please cite the original paper:
+
+```bibtex
+@article{depthanything3,
+ title={Depth Anything 3: Recovering the visual space from any views},
+ author={Haotong Lin and Sili Chen and Jun Hao Liew and Donny Y. Chen and Zhenyu Li and Guang Shi and Jiashi Feng and Bingyi Kang},
+ journal={arXiv preprint arXiv:2511.10647},
+ year={2025}
+}
+```
+
+If you specifically use features from this fork (caching, batching, MPS optimizations), you may additionally reference:
+
+```
+awesome-depth-anything-3: https://github.com/Aedelon/awesome-depth-anything-3
+```
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..737a2393eb961043b207a12b67874062682a9fea
--- /dev/null
+++ b/app.py
@@ -0,0 +1,59 @@
+#!/usr/bin/env python3
+# Copyright (c) Delanoe Pirard / Aedelon
+# Licensed under the Apache License, Version 2.0
+"""
+Hugging Face Spaces entry point for awesome-depth-anything-3.
+
+This file is the main entry point for the HF Spaces deployment.
+It launches the Gradio web interface with optimized settings for cloud deployment.
+"""
+
+import os
+import tempfile
+
+# Disable analytics and configure for HF Spaces
+os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
+os.environ["DA3_LOG_LEVEL"] = "WARNING"
+
+from depth_anything_3.app.gradio_app import DepthAnything3App
+
+
+def main():
+ """Launch the Gradio app for HF Spaces."""
+ # Use DA3-LARGE for good balance of quality and speed
+ workspace_dir = "/tmp/workspace"
+ gallery_dir = "/tmp/gallery"
+
+ # Create directories
+ os.makedirs(workspace_dir, exist_ok=True)
+ os.makedirs(gallery_dir, exist_ok=True)
+
+ app = DepthAnything3App(
+ model_dir="depth-anything/DA3-LARGE",
+ workspace_dir=workspace_dir,
+ gallery_dir=gallery_dir,
+ )
+
+ demo = app.create_app()
+
+ # Build allowed paths for Gradio file access
+ allowed_paths = [
+ os.getcwd(),
+ tempfile.gettempdir(),
+ workspace_dir,
+ gallery_dir,
+ "/tmp",
+ ]
+
+ # Launch for HF Spaces (theme/css already set in create_app via gr.Blocks())
+ demo.queue(max_size=10).launch(
+ server_name="0.0.0.0",
+ server_port=7860,
+ share=True,
+ show_error=True,
+ allowed_paths=allowed_paths,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/assets/examples/SOH/000.png b/assets/examples/SOH/000.png
new file mode 100644
index 0000000000000000000000000000000000000000..b5a93e0d4c9bf6b01e5e3f6906b98e2ddca3b95e
--- /dev/null
+++ b/assets/examples/SOH/000.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ea78c3b872b1e8b27de48cadf1d4a692cd42ddf5f72fcab78e2be2937935fb79
+size 1091690
diff --git a/assets/examples/SOH/010.png b/assets/examples/SOH/010.png
new file mode 100644
index 0000000000000000000000000000000000000000..c7352f9d9b6410b3050f3d665788e74194f643d6
--- /dev/null
+++ b/assets/examples/SOH/010.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c91a69d4e050e75e3760fbacda18a452b9abcf3065e6d6bd940b4a99d48f7982
+size 1134666
diff --git a/assets/examples/robot_unitree.mp4 b/assets/examples/robot_unitree.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..6dbbfd885b4ef3ff5580578ec38fd6deab6a27c7
--- /dev/null
+++ b/assets/examples/robot_unitree.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:99bc274f7613a665c6135085fe01691ebfaa9033101319071f37c550ab21d1ea
+size 1964268
diff --git a/assets/images/da3_radar.png b/assets/images/da3_radar.png
new file mode 100644
index 0000000000000000000000000000000000000000..3231985835e11a51e8b59c48ee84740fe4b7e06a
--- /dev/null
+++ b/assets/images/da3_radar.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d85aa2a89c1959e31ea627474360720b94a393126e8102399ce4dbc355d81d47
+size 214877
diff --git a/assets/images/demo320-2.gif b/assets/images/demo320-2.gif
new file mode 100644
index 0000000000000000000000000000000000000000..e9cff45d4fc754fb44da25f5f7572861ffb64038
--- /dev/null
+++ b/assets/images/demo320-2.gif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dc02d85064d875f7679c70b7156e7b5c7eef872895dbe45a241400c6f99e22f9
+size 18815873
diff --git a/benchmarks/__init__.py b/benchmarks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6b9bf54dd4699eb2299fd91b02c8a15f03307fb
--- /dev/null
+++ b/benchmarks/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (c) Delanoe Pirard / Aedelon
+# Licensed under the Apache License, Version 2.0
+"""Benchmark scripts for Depth Anything 3."""
diff --git a/benchmarks/comparative_benchmark.py b/benchmarks/comparative_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..d038fcafa2e2894d40468a064fe5ef31c76051e9
--- /dev/null
+++ b/benchmarks/comparative_benchmark.py
@@ -0,0 +1,436 @@
+#!/usr/bin/env python3
+# Copyright (c) Delanoe Pirard / Aedelon
+# Licensed under the Apache License, Version 2.0
+"""
+Comparative Benchmark: awesome-depth-anything-3 vs upstream (vanilla)
+
+Compares performance between the optimized fork and the original upstream.
+
+Usage:
+ python benchmarks/comparative_benchmark.py --device mps
+ python benchmarks/comparative_benchmark.py --device cuda
+ python benchmarks/comparative_benchmark.py --device all
+ python benchmarks/comparative_benchmark.py --quick
+"""
+
+import argparse
+import contextlib
+import gc
+import io
+import logging
+import os
+import shutil
+import sys
+import time
+import warnings
+
+# Suppress ALL logging before any imports
+logging.disable(logging.CRITICAL)
+os.environ["DA3_LOG_LEVEL"] = "CRITICAL"
+os.environ["PYTHONWARNINGS"] = "ignore"
+warnings.filterwarnings("ignore")
+
+import numpy as np
+import torch
+from PIL import Image
+
+# Suppress all loggers
+logging.getLogger("depth_anything_3").disabled = True
+logging.getLogger("dinov2").disabled = True
+logging.getLogger().setLevel(logging.CRITICAL)
+
+
+@contextlib.contextmanager
+def suppress_output():
+ """Context manager to suppress stdout and stderr."""
+ with contextlib.redirect_stdout(io.StringIO()), \
+ contextlib.redirect_stderr(io.StringIO()):
+ # Also suppress all loggers again
+ logging.disable(logging.CRITICAL)
+ yield
+
+# ============================================================================
+# CONFIGURATION
+# ============================================================================
+
+AWESOME_REPO = "/Users/aedelon/Workspace/awesome-depth-anything-3"
+UPSTREAM_REPO = "/Users/aedelon/Workspace/depth-anything-3-upstream"
+MODEL_NAME = "da3-large"
+
+
+# ============================================================================
+# UTILITIES
+# ============================================================================
+
+def cleanup():
+ gc.collect()
+ if torch.cuda.is_available():
+ torch.cuda.empty_cache()
+ torch.cuda.reset_peak_memory_stats()
+ if torch.backends.mps.is_available():
+ torch.mps.empty_cache()
+
+
+def sync_device(device):
+ if device.type == "cuda":
+ torch.cuda.synchronize()
+ elif device.type == "mps":
+ torch.mps.synchronize()
+
+
+def clear_modules():
+ """Clear depth_anything_3 from sys.modules."""
+ to_remove = [k for k in sys.modules.keys() if "depth_anything_3" in k]
+ for k in to_remove:
+ del sys.modules[k]
+
+
+def suppress_logging():
+ """Suppress all logging after module import."""
+ logging.disable(logging.CRITICAL)
+ try:
+ from depth_anything_3.utils.logger import logger
+ logger.level = 100
+ except:
+ pass
+
+
+def get_available_devices():
+ """Get available devices."""
+ devices = [torch.device("cpu")]
+ if torch.backends.mps.is_available():
+ devices.append(torch.device("mps"))
+ if torch.cuda.is_available():
+ devices.append(torch.device("cuda"))
+ return devices
+
+
+def get_device_name(device):
+ if device.type == "cuda":
+ return torch.cuda.get_device_name(device)
+ elif device.type == "mps":
+ return "Apple Silicon (MPS)"
+ return "CPU"
+
+
+# ============================================================================
+# BENCHMARK: UPSTREAM (VANILLA)
+# ============================================================================
+
+def benchmark_upstream(device, pil_images, process_res=504, runs=3):
+ """Benchmark upstream/vanilla depth-anything-3."""
+
+ # Setup path
+ clear_modules()
+ upstream_src = os.path.join(UPSTREAM_REPO, "src")
+ if upstream_src in sys.path:
+ sys.path.remove(upstream_src)
+ sys.path.insert(0, upstream_src)
+
+ with suppress_output():
+ from depth_anything_3.api import DepthAnything3
+ suppress_logging()
+
+ cleanup()
+
+ # Cold load
+ start = time.perf_counter()
+ model = DepthAnything3(model_name=MODEL_NAME)
+ model = model.to(device)
+ model.eval()
+ cold_load_time = time.perf_counter() - start
+
+ # Warmup
+ for _ in range(2):
+ model.inference(pil_images[:1], process_res=process_res)
+ sync_device(device)
+ cleanup()
+
+ # Benchmark inference
+ times = []
+ for _ in range(runs):
+ cleanup()
+ sync_device(device)
+ start = time.perf_counter()
+ model.inference(pil_images, process_res=process_res)
+ sync_device(device)
+ times.append(time.perf_counter() - start)
+
+ avg_time = np.mean(times)
+ std_time = np.std(times)
+ throughput = len(pil_images) / avg_time
+
+ del model
+ cleanup()
+
+ # Cleanup path
+ sys.path.remove(upstream_src)
+ clear_modules()
+
+ return {
+ "cold_load": cold_load_time,
+ "inference_time": avg_time,
+ "inference_std": std_time,
+ "throughput": throughput,
+ }
+
+
+# ============================================================================
+# BENCHMARK: AWESOME (OPTIMIZED)
+# ============================================================================
+
+def benchmark_awesome(device, pil_images, process_res=504, runs=3, use_cache=True):
+ """Benchmark awesome (optimized) depth-anything-3."""
+
+ # Setup path
+ clear_modules()
+ awesome_src = os.path.join(AWESOME_REPO, "src")
+ if awesome_src in sys.path:
+ sys.path.remove(awesome_src)
+ sys.path.insert(0, awesome_src)
+
+ with suppress_output():
+ from depth_anything_3.api import DepthAnything3
+ from depth_anything_3.cache import get_model_cache
+ suppress_logging()
+
+ # Clear cache if testing cold load
+ if not use_cache:
+ cache = get_model_cache()
+ cache.clear()
+
+ cleanup()
+
+ # Cold/warm load
+ start = time.perf_counter()
+ model = DepthAnything3(model_name=MODEL_NAME, device=device, use_cache=use_cache)
+ load_time = time.perf_counter() - start
+
+ # For cache test, do a second load
+ cached_load_time = None
+ if use_cache:
+ del model
+ cleanup()
+ start = time.perf_counter()
+ model = DepthAnything3(model_name=MODEL_NAME, device=device, use_cache=True)
+ cached_load_time = time.perf_counter() - start
+
+ # Warmup
+ for _ in range(2):
+ model.inference(pil_images[:1], process_res=process_res)
+ sync_device(device)
+ cleanup()
+
+ # Benchmark inference
+ times = []
+ for _ in range(runs):
+ cleanup()
+ sync_device(device)
+ start = time.perf_counter()
+ model.inference(pil_images, process_res=process_res)
+ sync_device(device)
+ times.append(time.perf_counter() - start)
+
+ avg_time = np.mean(times)
+ std_time = np.std(times)
+ throughput = len(pil_images) / avg_time
+
+ del model
+ cleanup()
+
+ # Cleanup path
+ sys.path.remove(awesome_src)
+ clear_modules()
+
+ return {
+ "cold_load": load_time,
+ "cached_load": cached_load_time,
+ "inference_time": avg_time,
+ "inference_std": std_time,
+ "throughput": throughput,
+ }
+
+
+# ============================================================================
+# MAIN
+# ============================================================================
+
+def run_comparison(device, batch_sizes, process_res=504, runs=3):
+ """Run comparison for a specific device."""
+
+ results = {}
+ temp_dir = "temp_compare"
+ os.makedirs(temp_dir, exist_ok=True)
+
+ try:
+ # Create test images
+ max_batch = max(batch_sizes)
+ pil_images = []
+ for i in range(max_batch):
+ img = Image.new("RGB", (1280, 720), color=(100 + i*10, 150, 200))
+ pil_images.append(img)
+
+ for batch_size in batch_sizes:
+ test_images = pil_images[:batch_size]
+ results[batch_size] = {}
+
+ print(f"\n Batch size: {batch_size}")
+ print(f" {'-'*50}")
+
+ # Upstream
+ print(f" Testing UPSTREAM (vanilla)...", end=" ", flush=True)
+ try:
+ upstream = benchmark_upstream(device, test_images, process_res, runs)
+ results[batch_size]["upstream"] = upstream
+ print(f"{upstream['throughput']:.2f} img/s")
+ except Exception as e:
+ print(f"ERROR: {e}")
+ results[batch_size]["upstream"] = None
+
+ # Awesome (no cache - fair comparison)
+ print(f" Testing AWESOME (no cache)...", end=" ", flush=True)
+ try:
+ awesome_nc = benchmark_awesome(device, test_images, process_res, runs, use_cache=False)
+ results[batch_size]["awesome_nocache"] = awesome_nc
+ print(f"{awesome_nc['throughput']:.2f} img/s")
+ except Exception as e:
+ print(f"ERROR: {e}")
+ results[batch_size]["awesome_nocache"] = None
+
+ # Awesome (with cache)
+ print(f" Testing AWESOME (cached)...", end=" ", flush=True)
+ try:
+ awesome_c = benchmark_awesome(device, test_images, process_res, runs, use_cache=True)
+ results[batch_size]["awesome_cached"] = awesome_c
+ print(f"{awesome_c['throughput']:.2f} img/s")
+ except Exception as e:
+ print(f"ERROR: {e}")
+ results[batch_size]["awesome_cached"] = None
+
+ finally:
+ shutil.rmtree(temp_dir, ignore_errors=True)
+
+ return results
+
+
+def print_results_table(results, device):
+ """Print formatted results table."""
+
+ print(f"\n{'='*70}")
+ print(f" RESULTS: {device.type.upper()}")
+ print(f"{'='*70}")
+
+ # Header
+ print(f"\n{'Batch':<8} {'Metric':<18} {'Upstream':<12} {'Awesome':<12} {'Speedup':<10}")
+ print("-" * 60)
+
+ for batch_size, data in sorted(results.items()):
+ upstream = data.get("upstream")
+ awesome = data.get("awesome_nocache") or data.get("awesome_cached")
+
+ if not upstream or not awesome:
+ continue
+
+ # Inference throughput
+ u_thr = upstream["throughput"]
+ a_thr = awesome["throughput"]
+ speedup = a_thr / u_thr if u_thr > 0 else 0
+ print(f"{batch_size:<8} {'Throughput (img/s)':<18} {u_thr:<12.2f} {a_thr:<12.2f} {speedup:<10.2f}x")
+
+ # Inference time
+ u_time = upstream["inference_time"] * 1000
+ a_time = awesome["inference_time"] * 1000
+ speedup = u_time / a_time if a_time > 0 else 0
+ print(f"{'':<8} {'Latency (ms)':<18} {u_time:<12.1f} {a_time:<12.1f} {speedup:<10.2f}x")
+
+ # Cold load time
+ u_load = upstream["cold_load"]
+ a_load = awesome["cold_load"]
+ speedup = u_load / a_load if a_load > 0 else 0
+ print(f"{'':<8} {'Cold load (s)':<18} {u_load:<12.2f} {a_load:<12.2f} {speedup:<10.2f}x")
+
+ # Cached load (awesome only)
+ cached = data.get("awesome_cached")
+ if cached and cached.get("cached_load"):
+ c_load = cached["cached_load"]
+ speedup = u_load / c_load if c_load > 0 else 0
+ print(f"{'':<8} {'Cached load (s)':<18} {'-':<12} {c_load:<12.3f} {speedup:<10.1f}x")
+
+ print()
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Comparative Benchmark: Awesome vs Upstream")
+ parser.add_argument("--device", "-d", type=str, default="auto",
+ choices=["auto", "cpu", "mps", "cuda", "all"],
+ help="Device to benchmark")
+ parser.add_argument("--batch-sizes", type=int, nargs="+", default=[1, 2, 4],
+ help="Batch sizes to test")
+ parser.add_argument("--runs", type=int, default=3, help="Number of runs per test")
+ parser.add_argument("--quick", action="store_true", help="Quick mode (fewer runs)")
+ args = parser.parse_args()
+
+ if args.quick:
+ args.batch_sizes = [1, 2]
+ args.runs = 2
+
+ # Determine devices
+ available = get_available_devices()
+ if args.device == "auto":
+ devices = [available[-1]]
+ elif args.device == "all":
+ devices = available
+ else:
+ requested = torch.device(args.device)
+ if requested in available:
+ devices = [requested]
+ else:
+ print(f"Device '{args.device}' not available. Available: {[d.type for d in available]}")
+ return
+
+ # Header
+ print("\n" + "=" * 70)
+ print(" COMPARATIVE BENCHMARK: AWESOME vs UPSTREAM (VANILLA)")
+ print("=" * 70)
+ print(f" Model: {MODEL_NAME}")
+ print(f" PyTorch: {torch.__version__}")
+ print(f" Batch sizes: {args.batch_sizes}")
+ print(f" Runs per test: {args.runs}")
+ print(f" Devices: {[d.type.upper() for d in devices]}")
+ for d in available:
+ status = "✓" if d in devices else "○"
+ print(f" {status} {d.type.upper()}: {get_device_name(d)}")
+ print("=" * 70)
+
+ all_results = {}
+
+ for device in devices:
+ print(f"\n{'#'*70}")
+ print(f" DEVICE: {device.type.upper()} ({get_device_name(device)})")
+ print(f"{'#'*70}")
+
+ results = run_comparison(device, args.batch_sizes, runs=args.runs)
+ all_results[device.type] = results
+ print_results_table(results, device)
+
+ # Final summary
+ print("\n" + "=" * 70)
+ print(" SUMMARY")
+ print("=" * 70)
+
+ for device_type, results in all_results.items():
+ print(f"\n {device_type.upper()}:")
+
+ for batch_size, data in sorted(results.items()):
+ upstream = data.get("upstream")
+ awesome = data.get("awesome_nocache")
+
+ if upstream and awesome:
+ speedup = awesome["throughput"] / upstream["throughput"]
+ print(f" Batch {batch_size}: {speedup:.2f}x faster inference")
+
+ print("\n" + "=" * 70 + "\n")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/benchmarks/flash_attention_benchmark.py b/benchmarks/flash_attention_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..f59d5c20a1e995f0a088f1c1c67de93e5104832f
--- /dev/null
+++ b/benchmarks/flash_attention_benchmark.py
@@ -0,0 +1,488 @@
+#!/usr/bin/env python3
+# Copyright (c) Delanoe Pirard / Aedelon - Apache 2.0
+"""
+Flash Attention Benchmark for Depth Anything 3.
+
+Provides clear performance comparison with tables and analysis.
+
+Usage:
+ python benchmarks/flash_attention_benchmark.py
+ python benchmarks/flash_attention_benchmark.py --detailed
+"""
+
+import argparse
+import gc
+import os
+import sys
+import time
+from dataclasses import dataclass
+
+import torch
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src"))
+
+from depth_anything_3.model.dinov2.layers import (
+ FLASH_ATTN_AVAILABLE,
+ FLASH_ATTN_VERSION,
+ Attention,
+)
+
+
+@dataclass
+class BenchmarkConfig:
+ """Configuration for a benchmark test case."""
+
+ name: str
+ seq_len: int
+ batch_size: int
+ embed_dim: int
+ num_heads: int
+ image_size: str # Description of corresponding image size
+
+ @property
+ def description(self):
+ return f"{self.name} ({self.image_size})"
+
+
+# Depth Anything 3 model configurations
+DA3_CONFIGS = {
+ "vitb": {"embed_dim": 768, "num_heads": 12, "depth": 12},
+ "vitl": {"embed_dim": 1024, "num_heads": 16, "depth": 24},
+ "vitg": {"embed_dim": 1536, "num_heads": 24, "depth": 40},
+}
+
+
+def get_device_info():
+ """Get device information."""
+ if torch.cuda.is_available():
+ device = torch.device("cuda")
+ device_name = torch.cuda.get_device_name()
+ memory_gb = torch.cuda.get_device_properties(0).total_memory / 1024**3
+ compute_cap = torch.cuda.get_device_capability()
+ return {
+ "type": "cuda",
+ "device": device,
+ "name": device_name,
+ "memory_gb": memory_gb,
+ "compute_capability": f"{compute_cap[0]}.{compute_cap[1]}",
+ }
+ elif torch.backends.mps.is_available():
+ return {
+ "type": "mps",
+ "device": torch.device("mps"),
+ "name": "Apple Silicon",
+ "memory_gb": None,
+ "compute_capability": None,
+ }
+ else:
+ return {
+ "type": "cpu",
+ "device": torch.device("cpu"),
+ "name": "CPU",
+ "memory_gb": None,
+ "compute_capability": None,
+ }
+
+
+def benchmark_attention(attn_module, x, warmup=5, runs=20):
+ """Run benchmark for a single attention module."""
+ device = x.device
+
+ # Warmup
+ with torch.no_grad():
+ for _ in range(warmup):
+ _ = attn_module(x)
+ if device.type == "cuda":
+ torch.cuda.synchronize()
+
+ # Reset memory tracking
+ if device.type == "cuda":
+ torch.cuda.reset_peak_memory_stats()
+
+ # Benchmark
+ times = []
+ with torch.no_grad():
+ for _ in range(runs):
+ if device.type == "cuda":
+ torch.cuda.synchronize()
+ start = time.perf_counter()
+ _ = attn_module(x)
+ if device.type == "cuda":
+ torch.cuda.synchronize()
+ times.append((time.perf_counter() - start) * 1000)
+
+ # Memory
+ peak_mem_mb = 0
+ if device.type == "cuda":
+ peak_mem_mb = torch.cuda.max_memory_allocated() / 1024 / 1024
+
+ times_tensor = torch.tensor(times)
+ return {
+ "mean_ms": times_tensor.mean().item(),
+ "std_ms": times_tensor.std().item(),
+ "min_ms": times_tensor.min().item(),
+ "peak_mem_mb": peak_mem_mb,
+ }
+
+
+def print_header():
+ """Print benchmark header."""
+ print("\n" + "=" * 80)
+ print(" " * 20 + "FLASH ATTENTION BENCHMARK - DEPTH ANYTHING 3")
+ print("=" * 80 + "\n")
+
+
+def get_sdpa_backend_info():
+ """Get info about which SDPA backend is being used."""
+ info = {}
+ if torch.cuda.is_available():
+ from torch.backends.cuda import (
+ flash_sdp_enabled,
+ mem_efficient_sdp_enabled,
+ math_sdp_enabled,
+ )
+ info["flash_sdp"] = flash_sdp_enabled()
+ info["mem_efficient_sdp"] = mem_efficient_sdp_enabled()
+ info["math_sdp"] = math_sdp_enabled()
+ return info
+
+
+def print_device_info(device_info):
+ """Print device information."""
+ print("📊 HARDWARE CONFIGURATION")
+ print("─" * 80)
+ print(f" Device Type : {device_info['type'].upper()}")
+ print(f" Device Name : {device_info['name']}")
+ if device_info["memory_gb"]:
+ print(f" Memory : {device_info['memory_gb']:.1f} GB")
+ if device_info["compute_capability"]:
+ print(f" Compute Cap. : {device_info['compute_capability']}")
+ cc = float(device_info["compute_capability"])
+ if cc >= 7.5:
+ print(f" ✅ Flash Attention supported (≥7.5)")
+ else:
+ print(f" ❌ Flash Attention requires ≥7.5")
+
+ # SDPA backend info
+ sdpa_info = get_sdpa_backend_info()
+ if sdpa_info:
+ print(f"\n PyTorch SDPA Backends:")
+ print(f" Flash SDP : {'✅ Enabled' if sdpa_info.get('flash_sdp') else '❌ Disabled'}")
+ print(f" MemEfficient : {'✅ Enabled' if sdpa_info.get('mem_efficient_sdp') else '❌ Disabled'}")
+ print(f" Math SDP : {'✅ Enabled' if sdpa_info.get('math_sdp') else '❌ Disabled'}")
+
+ if sdpa_info.get('flash_sdp'):
+ print(f"\n ⚡ PyTorch SDPA uses Flash Attention internally!")
+ print(f" (No need for flash-attn package with PyTorch >= 2.2)")
+
+ print(f"\n flash-attn pkg : {'✅ Installed v' + FLASH_ATTN_VERSION if FLASH_ATTN_AVAILABLE else '❌ Not installed (optional)'}")
+ print()
+
+
+def print_table_header():
+ """Print benchmark table header."""
+ print(
+ "┌──────────────────────────┬──────────────┬──────────────┬──────────────┬────────────┐"
+ )
+ print(
+ "│ Configuration │ flash_attn │ sdpa │ manual │ Speedup │"
+ )
+ print(
+ "├──────────────────────────┼──────────────┼──────────────┼──────────────┼────────────┤"
+ )
+
+
+def print_table_row(config_desc, results, baseline="sdpa"):
+ """Print a benchmark result row."""
+ backends = ["flash_attn", "sdpa", "manual"]
+
+ # Format times
+ time_strs = []
+ for backend in backends:
+ if backend in results and results[backend]:
+ time_ms = results[backend]["mean_ms"]
+ time_strs.append(f"{time_ms:6.2f} ms")
+ else:
+ time_strs.append(" N/A")
+
+ # Calculate speedup
+ speedup_str = " -"
+ if "flash_attn" in results and results["flash_attn"] and baseline in results:
+ if results[baseline]:
+ speedup = results[baseline]["mean_ms"] / results["flash_attn"]["mean_ms"]
+ speedup_str = f" {speedup:.2f}x ⚡" if speedup > 1.1 else f" {speedup:.2f}x"
+
+ print(
+ f"│ {config_desc:24s} │ {time_strs[0]:12s} │ {time_strs[1]:12s} │ {time_strs[2]:12s} │ {speedup_str:10s} │"
+ )
+
+
+def print_table_footer():
+ """Print benchmark table footer."""
+ print(
+ "└──────────────────────────┴──────────────┴──────────────┴──────────────┴────────────┘"
+ )
+
+
+def print_model_analysis(model_name, config, results, num_layers):
+ """Print detailed analysis for a specific model."""
+ if "flash_attn" not in results or not results["flash_attn"]:
+ return
+
+ flash_time = results["flash_attn"]["mean_ms"]
+ sdpa_time = results["sdpa"]["mean_ms"] if "sdpa" in results else flash_time
+
+ speedup = sdpa_time / flash_time
+ time_saved_per_layer = (sdpa_time - flash_time) / num_layers
+ total_time_saved = time_saved_per_layer * num_layers
+
+ print(f"\n 📈 {model_name} Analysis:")
+ print(f" • Attention time per layer: {flash_time:.2f} ms (flash) vs {sdpa_time:.2f} ms (sdpa)")
+ print(f" • Time saved per layer: {time_saved_per_layer:.2f} ms")
+ print(f" • Total time saved ({num_layers} layers): {total_time_saved:.1f} ms")
+ print(f" • Speedup: {speedup:.2f}x on attention")
+
+ # Estimate full inference impact
+ # Attention is ~15-20% of total inference time
+ attn_fraction = 0.175
+ overall_speedup = 1 / (1 - attn_fraction + attn_fraction / speedup)
+ overall_improvement = (1 - 1 / overall_speedup) * 100
+
+ print(
+ f" • Estimated full inference speedup: {overall_speedup:.2f}x (~{overall_improvement:.1f}% faster)"
+ )
+
+
+def run_benchmark(test_configs, backends, warmup=5, runs=20, detailed=False):
+ """Run complete benchmark suite."""
+ device_info = get_device_info()
+ device = device_info["device"]
+ dtype = torch.float16 if device.type == "cuda" else torch.float32
+
+ print_header()
+ print_device_info(device_info)
+
+ # Filter backends based on availability
+ available_backends = []
+ if FLASH_ATTN_AVAILABLE and device.type == "cuda":
+ available_backends.append("flash_attn")
+ available_backends.append("sdpa")
+ if detailed:
+ available_backends.append("manual")
+
+ all_results = {}
+
+ # Run benchmarks by model
+ for model_name, model_config in DA3_CONFIGS.items():
+ print(f"\n🔬 MODEL: {model_name.upper()} (dim={model_config['embed_dim']}, heads={model_config['num_heads']}, depth={model_config['depth']})")
+ print("─" * 80)
+ print_table_header()
+
+ model_results = {}
+
+ for test_config in test_configs:
+ # Adjust config for this model
+ config = BenchmarkConfig(
+ name=test_config.name,
+ seq_len=test_config.seq_len,
+ batch_size=test_config.batch_size,
+ embed_dim=model_config["embed_dim"],
+ num_heads=model_config["num_heads"],
+ image_size=test_config.image_size,
+ )
+
+ x = torch.randn(
+ config.batch_size, config.seq_len, config.embed_dim, device=device, dtype=dtype
+ )
+
+ results = {}
+ for backend in available_backends:
+ gc.collect()
+ if device.type == "cuda":
+ torch.cuda.empty_cache()
+
+ try:
+ attn = Attention(
+ dim=config.embed_dim,
+ num_heads=config.num_heads,
+ attn_backend=backend,
+ ).to(device, dtype)
+ attn.eval()
+
+ result = benchmark_attention(attn, x, warmup=warmup, runs=runs)
+ results[backend] = result
+
+ del attn
+ except Exception as e:
+ results[backend] = None
+ if detailed:
+ print(f" {backend} failed: {e}")
+
+ model_results[config.name] = results
+ print_table_row(config.description, results)
+
+ print_table_footer()
+
+ # Analysis for this model
+ if detailed and model_results:
+ # Use medium config for analysis
+ medium_key = next(
+ (k for k in model_results.keys() if "1024" in k.lower() or "medium" in k.lower()),
+ list(model_results.keys())[0],
+ )
+ print_model_analysis(
+ model_name.upper(),
+ test_configs[0],
+ model_results[medium_key],
+ model_config["depth"],
+ )
+
+ all_results[model_name] = model_results
+
+ # Final summary
+ print("\n" + "=" * 80)
+ print("📋 SUMMARY & RECOMMENDATIONS")
+ print("=" * 80)
+
+ sdpa_info = get_sdpa_backend_info()
+
+ if device.type == "cuda":
+ # Check if PyTorch SDPA has Flash enabled
+ if sdpa_info.get('flash_sdp'):
+ print("\n✅ Flash Attention is ACTIVE via PyTorch SDPA!")
+ print("\n Your setup:")
+ print(f" • PyTorch {torch.__version__} with native Flash Attention")
+ print(" • SDPA backend: Flash SDP ⚡")
+ print(" • No additional packages needed!")
+ print("\n Benefits you're already getting:")
+ print(" • 2-4x faster attention vs manual implementation")
+ print(" • Memory-efficient attention computation")
+ print(" • Automatic kernel selection per input size")
+
+ if FLASH_ATTN_AVAILABLE:
+ print(f"\n ℹ️ flash-attn v{FLASH_ATTN_VERSION} also installed")
+ print(" (May provide slight additional optimization in some cases)")
+ else:
+ print("\n ℹ️ flash-attn package: Not needed!")
+ print(" PyTorch >= 2.2 includes Flash Attention natively.")
+
+ elif FLASH_ATTN_AVAILABLE:
+ print("\n✅ Flash Attention is ACTIVE via flash-attn package")
+ print(f"\n Using flash-attn v{FLASH_ATTN_VERSION}")
+ print("\n Benefits:")
+ print(" • 2-3x faster attention computation")
+ print(" • ~15-25% overall inference speedup")
+ print(" • Lower memory usage")
+
+ else:
+ print("\n⚠️ Flash Attention not available")
+ print("\n Options to enable:")
+ print(" 1. Upgrade PyTorch to >= 2.2 (recommended)")
+ print(" 2. Install flash-attn: pip install flash-attn --no-build-isolation")
+
+ elif device.type == "mps":
+ print("\n📱 Apple Silicon (MPS) detected")
+ print("\n • Flash Attention not available for MPS")
+ print(" • PyTorch SDPA uses optimized Metal kernels")
+ print(" • Already running at optimal speed for your hardware")
+
+ else:
+ print("\n💻 CPU detected")
+ print("\n • Consider using GPU for faster inference")
+ print(" • Flash Attention is CUDA-only")
+
+ # Print SDPA vs Manual speedup summary
+ print("\n" + "─" * 80)
+ print("⚡ PERFORMANCE COMPARISON")
+ print("─" * 80)
+ print("\n SDPA vs Manual attention speedup (per layer):")
+
+ for model_name, model_results in all_results.items():
+ if model_results:
+ # Get XLarge config results for most impact
+ xlarge_key = next((k for k in model_results.keys() if "xlarge" in k.lower()), list(model_results.keys())[-1])
+ if xlarge_key in model_results:
+ res = model_results[xlarge_key]
+ if res.get("sdpa") and res.get("manual"):
+ speedup = res["manual"]["mean_ms"] / res["sdpa"]["mean_ms"]
+ print(f" • {model_name.upper():6s}: {speedup:.1f}x faster (sdpa: {res['sdpa']['mean_ms']:.2f}ms vs manual: {res['manual']['mean_ms']:.2f}ms)")
+
+ print("\n" + "=" * 80)
+ print()
+
+ return all_results
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Flash Attention benchmark for DA3")
+ parser.add_argument(
+ "--detailed",
+ action="store_true",
+ help="Show detailed analysis and include manual backend",
+ )
+ parser.add_argument(
+ "--warmup",
+ type=int,
+ default=5,
+ help="Warmup iterations (default: 5)",
+ )
+ parser.add_argument(
+ "--runs",
+ type=int,
+ default=20,
+ help="Benchmark runs (default: 20)",
+ )
+
+ args = parser.parse_args()
+
+ # Test configurations based on common image sizes
+ test_configs = [
+ BenchmarkConfig(
+ name="Small",
+ seq_len=256,
+ batch_size=1,
+ embed_dim=768, # Will be overridden per model
+ num_heads=12, # Will be overridden per model
+ image_size="392px image",
+ ),
+ BenchmarkConfig(
+ name="Medium",
+ seq_len=529,
+ batch_size=1,
+ embed_dim=768,
+ num_heads=12,
+ image_size="518px image",
+ ),
+ BenchmarkConfig(
+ name="Large",
+ seq_len=1024,
+ batch_size=1,
+ embed_dim=768,
+ num_heads=12,
+ image_size="742px image",
+ ),
+ BenchmarkConfig(
+ name="XLarge",
+ seq_len=1369,
+ batch_size=1,
+ embed_dim=768,
+ num_heads=12,
+ image_size="1024px image",
+ ),
+ ]
+
+ backends = ["flash_attn", "sdpa"]
+ if args.detailed:
+ backends.append("manual")
+
+ run_benchmark(
+ test_configs=test_configs,
+ backends=backends,
+ warmup=args.warmup,
+ runs=args.runs,
+ detailed=args.detailed,
+ )
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/benchmarks/full_benchmark.py b/benchmarks/full_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..56811bd2424d144bc109b88e94352a4ade647bc8
--- /dev/null
+++ b/benchmarks/full_benchmark.py
@@ -0,0 +1,696 @@
+#!/usr/bin/env python3
+# Copyright (c) 2025 Delanoe Pirard / Aedelon - Apache 2.0
+"""
+Full Benchmark Suite for Depth Anything 3
+
+Tests ALL optimization combinations for each device (CPU, MPS, CUDA).
+
+Optimizations tested:
+- Preprocessing: CPU (PIL) vs GPU (NVJPEG on CUDA)
+- Attention: SDPA (Flash Attention) vs Manual
+
+Usage:
+ python benchmarks/full_benchmark.py # Best device only
+ python benchmarks/full_benchmark.py -d all # All devices
+ python benchmarks/full_benchmark.py -d cuda # CUDA only
+ python benchmarks/full_benchmark.py --quick # Quick mode
+"""
+
+import argparse
+import gc
+import logging
+import os
+import shutil
+import sys
+import time
+import warnings
+from dataclasses import dataclass
+from typing import Dict, List, Optional
+
+# Suppress ALL logging before any imports
+logging.disable(logging.CRITICAL)
+os.environ["DA3_LOG_LEVEL"] = "ERROR"
+warnings.filterwarnings("ignore")
+
+import numpy as np
+import torch
+from PIL import Image
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src"))
+
+# Suppress depth_anything_3 logger specifically
+logging.getLogger("depth_anything_3").disabled = True
+logging.getLogger("dinov2").disabled = True
+
+
+# ============================================================================
+# STYLES
+# ============================================================================
+
+class Style:
+ CYAN = "\033[96m"
+ GREEN = "\033[92m"
+ YELLOW = "\033[93m"
+ RED = "\033[91m"
+ BOLD = "\033[1m"
+ DIM = "\033[2m"
+ RESET = "\033[0m"
+
+
+def colored(text, color, bold=False):
+ prefix = Style.BOLD if bold else ""
+ return f"{prefix}{color}{text}{Style.RESET}"
+
+
+# ============================================================================
+# UTILITIES
+# ============================================================================
+
+def cleanup():
+ gc.collect()
+ if torch.cuda.is_available():
+ torch.cuda.empty_cache()
+ torch.cuda.reset_peak_memory_stats()
+ if torch.backends.mps.is_available():
+ torch.mps.empty_cache()
+
+
+def sync_device(device):
+ if device.type == "cuda":
+ torch.cuda.synchronize()
+ elif device.type == "mps":
+ torch.mps.synchronize()
+
+
+def get_available_devices() -> List[torch.device]:
+ """Get all available devices for benchmarking."""
+ devices = [torch.device("cpu")]
+ if torch.backends.mps.is_available():
+ devices.append(torch.device("mps"))
+ if torch.cuda.is_available():
+ devices.append(torch.device("cuda"))
+ return devices
+
+
+def get_device_name(device: torch.device) -> str:
+ """Get human-readable device name."""
+ if device.type == "cuda":
+ return torch.cuda.get_device_name(device)
+ elif device.type == "mps":
+ return "Apple Silicon (MPS)"
+ else:
+ import platform
+ return f"CPU ({platform.processor() or 'Unknown'})"
+
+
+# ============================================================================
+# DATA CLASSES
+# ============================================================================
+
+@dataclass
+class BenchmarkResult:
+ """Single benchmark result."""
+ mean_ms: float
+ std_ms: float
+ fps: float
+
+ @classmethod
+ def from_times(cls, times: List[float], batch_size: int = 1):
+ mean_ms = np.mean(times)
+ std_ms = np.std(times)
+ fps = 1000 / mean_ms * batch_size
+ return cls(mean_ms=mean_ms, std_ms=std_ms, fps=fps)
+
+
+@dataclass
+class OptimizationConfig:
+ """Configuration for a specific optimization combination."""
+ name: str
+ preprocessing: str # "cpu" or "gpu"
+ attention: str # "sdpa" or "manual"
+ description: str
+
+ @property
+ def short_name(self) -> str:
+ prep = "GPU" if self.preprocessing == "gpu" else "CPU"
+ attn = "SDPA" if self.attention == "sdpa" else "Manual"
+ return f"{prep}+{attn}"
+
+
+# ============================================================================
+# BENCHMARK FUNCTIONS
+# ============================================================================
+
+def get_optimization_configs(device: torch.device) -> List[OptimizationConfig]:
+ """Get all valid optimization configurations for a device."""
+ configs = []
+
+ if device.type == "cuda":
+ # CUDA: All 4 combinations
+ configs = [
+ OptimizationConfig("gpu_sdpa", "gpu", "sdpa", "GPU Decode (NVJPEG) + SDPA (Flash)"),
+ OptimizationConfig("gpu_manual", "gpu", "manual", "GPU Decode (NVJPEG) + Manual Attn"),
+ OptimizationConfig("cpu_sdpa", "cpu", "sdpa", "CPU Decode (PIL) + SDPA (Flash)"),
+ OptimizationConfig("cpu_manual", "cpu", "manual", "CPU Decode (PIL) + Manual Attn"),
+ ]
+ elif device.type == "mps":
+ # MPS: CPU preprocessing is better, 2 combinations
+ configs = [
+ OptimizationConfig("cpu_sdpa", "cpu", "sdpa", "CPU Decode (PIL) + SDPA"),
+ OptimizationConfig("cpu_manual", "cpu", "manual", "CPU Decode (PIL) + Manual Attn"),
+ ]
+ else:
+ # CPU: 2 combinations
+ configs = [
+ OptimizationConfig("cpu_sdpa", "cpu", "sdpa", "SDPA Attention"),
+ OptimizationConfig("cpu_manual", "cpu", "manual", "Manual Attention"),
+ ]
+
+ return configs
+
+
+def benchmark_preprocessing_detailed(device: torch.device, runs: int = 5) -> Dict:
+ """Benchmark preprocessing in detail."""
+ from depth_anything_3.utils.io.input_processor import InputProcessor
+ from depth_anything_3.utils.io.gpu_input_processor import GPUInputProcessor
+
+ results = {}
+ temp_dir = "temp_bench_preproc"
+
+ sizes = [
+ ("720p", 1280, 720),
+ ("1080p", 1920, 1080),
+ ("4K", 3840, 2160),
+ ]
+
+ os.makedirs(temp_dir, exist_ok=True)
+
+ try:
+ cpu_proc = InputProcessor()
+ gpu_proc = None
+ if device.type == "cuda":
+ gpu_proc = GPUInputProcessor(device=device)
+
+ for name, w, h in sizes:
+ results[name] = {}
+
+ # Create test files
+ files = []
+ pil_imgs = []
+ for i in range(4):
+ img = Image.new("RGB", (w, h), color=(100 + i*10, 150, 200))
+ fpath = f"{temp_dir}/{name}_{i}.jpg"
+ img.save(fpath, quality=95)
+ files.append(fpath)
+ pil_imgs.append(img.copy())
+
+ # CPU benchmark
+ cleanup()
+ for _ in range(2):
+ cpu_proc(image=pil_imgs, process_res=518, num_workers=8)
+
+ times = []
+ for _ in range(runs):
+ start = time.perf_counter()
+ cpu_proc(image=pil_imgs, process_res=518, num_workers=8)
+ times.append((time.perf_counter() - start) * 1000)
+ results[name]["cpu"] = BenchmarkResult.from_times(times, batch_size=4)
+
+ # GPU benchmark (NVJPEG for CUDA)
+ if gpu_proc and gpu_proc.use_gpu:
+ cleanup()
+ for _ in range(2):
+ gpu_proc(image=files, process_res=518, num_workers=1)
+ sync_device(device)
+
+ times = []
+ for _ in range(runs):
+ sync_device(device)
+ start = time.perf_counter()
+ gpu_proc(image=files, process_res=518, num_workers=1)
+ sync_device(device)
+ times.append((time.perf_counter() - start) * 1000)
+ results[name]["gpu"] = BenchmarkResult.from_times(times, batch_size=4)
+
+ finally:
+ shutil.rmtree(temp_dir, ignore_errors=True)
+
+ return results
+
+
+def benchmark_attention_detailed(device: torch.device, runs: int = 10) -> Dict:
+ """Benchmark attention backends in detail."""
+ from depth_anything_3.model.dinov2.layers import Attention
+
+ results = {}
+ dtype = torch.float16 if device.type == "cuda" else torch.float32
+
+ configs = [
+ ("ViT-S (518px)", 384, 6, 529),
+ ("ViT-L (518px)", 1024, 16, 529),
+ ("ViT-L (770px)", 1024, 16, 1156),
+ ]
+
+ for name, dim, heads, seq_len in configs:
+ results[name] = {}
+ x = torch.randn(1, seq_len, dim, device=device, dtype=dtype)
+
+ for backend in ["sdpa", "manual"]:
+ cleanup()
+ attn = Attention(dim=dim, num_heads=heads, attn_backend=backend).to(device, dtype)
+ attn.eval()
+
+ # Warmup
+ with torch.no_grad():
+ for _ in range(3):
+ attn(x)
+ sync_device(device)
+
+ # Benchmark
+ times = []
+ with torch.no_grad():
+ for _ in range(runs):
+ sync_device(device)
+ start = time.perf_counter()
+ attn(x)
+ sync_device(device)
+ times.append((time.perf_counter() - start) * 1000)
+
+ results[name][backend] = BenchmarkResult.from_times(times)
+ del attn
+
+ return results
+
+
+def benchmark_inference_matrix(
+ device: torch.device,
+ models: List[str],
+ runs: int = 3,
+) -> Dict:
+ """Benchmark all optimization combinations for inference."""
+ from depth_anything_3.api import DepthAnything3
+
+ results = {}
+ temp_dir = "temp_bench_infer"
+ configs = get_optimization_configs(device)
+
+ os.makedirs(temp_dir, exist_ok=True)
+
+ # Create test images (720p)
+ img_paths = []
+ pil_imgs = []
+ for i in range(4):
+ img = Image.new("RGB", (1280, 720), color=(100 + i*20, 150, 200))
+ path = f"{temp_dir}/test_{i}.jpg"
+ img.save(path, quality=95)
+ img_paths.append(path)
+ pil_imgs.append(img.copy())
+
+ try:
+ for model_name in models:
+ results[model_name] = {}
+
+ for config in configs:
+ cleanup()
+
+ # Set attention backend
+ os.environ["DA3_ATTENTION_BACKEND"] = config.attention
+
+ # Load model fresh (to apply attention backend)
+ model = DepthAnything3(
+ model_name=model_name,
+ device=device,
+ use_cache=False,
+ )
+
+ # Choose input based on preprocessing
+ if config.preprocessing == "gpu" and device.type == "cuda":
+ test_input = img_paths[:1] # File paths for NVJPEG
+ else:
+ test_input = pil_imgs[:1] # PIL for CPU preprocessing
+
+ # Warmup
+ for _ in range(3):
+ model.inference(test_input, process_res=518)
+ sync_device(device)
+
+ # Benchmark
+ times = []
+ for _ in range(runs):
+ sync_device(device)
+ start = time.perf_counter()
+ model.inference(test_input, process_res=518)
+ sync_device(device)
+ times.append((time.perf_counter() - start) * 1000)
+
+ results[model_name][config.name] = {
+ "result": BenchmarkResult.from_times(times, batch_size=1),
+ "config": config,
+ }
+
+ del model
+ cleanup()
+
+ finally:
+ shutil.rmtree(temp_dir, ignore_errors=True)
+
+ return results
+
+
+# ============================================================================
+# DISPLAY FUNCTIONS
+# ============================================================================
+
+def print_header(title: str):
+ """Print section header."""
+ print()
+ print(colored("═" * 70, Style.CYAN))
+ print(colored("║", Style.CYAN) + colored(f" {title}", Style.BOLD).center(77) + colored("║", Style.CYAN))
+ print(colored("═" * 70, Style.CYAN))
+
+
+def print_subheader(title: str):
+ """Print subsection header."""
+ print()
+ print(colored(f"▶ {title}", Style.YELLOW, bold=True))
+ print(colored("─" * 70, Style.DIM))
+
+
+def format_speedup(speedup: float) -> str:
+ """Format speedup with color."""
+ if speedup >= 1.5:
+ return colored(f"{speedup:.2f}x", Style.GREEN, bold=True)
+ elif speedup >= 1.1:
+ return colored(f"{speedup:.2f}x", Style.GREEN)
+ elif speedup >= 0.95:
+ return f"{speedup:.2f}x"
+ else:
+ return colored(f"{speedup:.2f}x", Style.RED)
+
+
+def print_preprocessing_results(results: Dict, device: torch.device):
+ """Print preprocessing benchmark results."""
+ print_subheader("PREPROCESSING (4 images batch)")
+
+ has_gpu = any("gpu" in r for r in results.values())
+
+ if has_gpu:
+ print(f" {'Resolution':<12} {'CPU (PIL)':<14} {'GPU (NVJPEG)':<14} {'Speedup':<10}")
+ print(f" {'-'*50}")
+
+ for name, data in results.items():
+ cpu_ms = data["cpu"].mean_ms
+ if "gpu" in data:
+ gpu_ms = data["gpu"].mean_ms
+ speedup = cpu_ms / gpu_ms
+ print(f" {name:<12} {cpu_ms:>8.1f} ms {gpu_ms:>8.1f} ms {format_speedup(speedup)}")
+ else:
+ print(f" {name:<12} {cpu_ms:>8.1f} ms {'N/A':<14}")
+ else:
+ print(f" {'Resolution':<12} {'CPU (PIL)':<14}")
+ print(f" {'-'*30}")
+ for name, data in results.items():
+ cpu_ms = data["cpu"].mean_ms
+ print(f" {name:<12} {cpu_ms:>8.1f} ms")
+
+ # Summary
+ if has_gpu:
+ speedups = []
+ for data in results.values():
+ if "gpu" in data:
+ speedups.append(data["cpu"].mean_ms / data["gpu"].mean_ms)
+ if speedups:
+ avg = np.mean(speedups)
+ print()
+ print(f" {colored('→', Style.GREEN)} GPU preprocessing avg {colored(f'{avg:.1f}x', Style.GREEN, bold=True)} faster")
+
+
+def print_attention_results(results: Dict, device: torch.device):
+ """Print attention benchmark results."""
+ print_subheader("ATTENTION (per layer forward pass)")
+
+ print(f" {'Config':<18} {'SDPA':<12} {'Manual':<12} {'Speedup':<10}")
+ print(f" {'-'*52}")
+
+ for name, data in results.items():
+ sdpa_ms = data["sdpa"].mean_ms
+ manual_ms = data["manual"].mean_ms
+ speedup = manual_ms / sdpa_ms
+ print(f" {name:<18} {sdpa_ms:>6.3f} ms {manual_ms:>6.3f} ms {format_speedup(speedup)}")
+
+ # Summary
+ speedups = [d["manual"].mean_ms / d["sdpa"].mean_ms for d in results.values()]
+ avg = np.mean(speedups)
+ print()
+ print(f" {colored('→', Style.GREEN)} SDPA avg {colored(f'{avg:.1f}x', Style.GREEN, bold=True)} faster than manual")
+
+ # Check Flash SDP
+ if device.type == "cuda":
+ from torch.backends.cuda import flash_sdp_enabled
+ if flash_sdp_enabled():
+ print(f" {colored('→', Style.GREEN)} Flash Attention: {colored('ENABLED', Style.GREEN, bold=True)} (PyTorch native)")
+
+
+def print_inference_matrix(results: Dict, device: torch.device):
+ """Print inference benchmark matrix."""
+ print_subheader("END-TO-END INFERENCE (720p input, batch=1)")
+
+ configs = get_optimization_configs(device)
+
+ # Header
+ header = f" {'Model':<12}"
+ for cfg in configs:
+ header += f" {cfg.short_name:<14}"
+ header += " Best"
+ print(header)
+ print(f" {'-'*(14 + 15*len(configs) + 6)}")
+
+ # Results per model
+ for model_name, model_results in results.items():
+ row = f" {model_name:<12}"
+
+ best_fps = 0
+ best_config = None
+ worst_fps = float('inf')
+
+ for cfg in configs:
+ if cfg.name in model_results:
+ result = model_results[cfg.name]["result"]
+ fps = result.fps
+ row += f" {fps:>6.1f} img/s "
+
+ if fps > best_fps:
+ best_fps = fps
+ best_config = cfg
+ if fps < worst_fps:
+ worst_fps = fps
+ else:
+ row += f" {'N/A':<14}"
+
+ # Best indicator
+ if best_config:
+ row += f" {colored(best_config.short_name, Style.GREEN, bold=True)}"
+
+ print(row)
+
+ # Summary
+ print()
+ print(f" {Style.DIM}Legend: GPU=NVJPEG decode, CPU=PIL decode, SDPA=Flash Attention{Style.RESET}")
+
+
+def print_device_summary(
+ device: torch.device,
+ preproc_results: Dict,
+ attn_results: Dict,
+ infer_results: Dict,
+):
+ """Print summary for a device."""
+ print()
+ print(colored("─" * 70, Style.CYAN))
+ print(colored(f" {device.type.upper()} - OPTIMIZATION SUMMARY", Style.BOLD))
+ print(colored("─" * 70, Style.CYAN))
+
+ # Best configuration
+ if infer_results:
+ print()
+ print(f" {colored('Best configuration per model:', Style.CYAN)}")
+
+ for model_name, model_results in infer_results.items():
+ if not model_results:
+ continue
+
+ best_name = max(model_results.keys(), key=lambda k: model_results[k]["result"].fps)
+ best = model_results[best_name]
+ worst_name = min(model_results.keys(), key=lambda k: model_results[k]["result"].fps)
+ worst = model_results[worst_name]
+
+ speedup = best["result"].fps / worst["result"].fps if worst["result"].fps > 0 else 1
+
+ print(f" {model_name:<12} {colored(best['config'].description, Style.GREEN)}")
+ print(f" {'':<12} {best['result'].fps:.1f} img/s ({speedup:.1f}x vs worst)")
+
+ # Recommendations
+ print()
+ print(f" {colored('Recommendations:', Style.CYAN)}")
+
+ if device.type == "cuda":
+ print(f" ✓ Use {colored('GPU preprocessing (NVJPEG)', Style.GREEN)} for file inputs")
+ print(f" ✓ {colored('SDPA (Flash Attention)', Style.GREEN)} is enabled by default")
+ print(f" ✓ Pass file paths (not PIL images) to leverage NVJPEG")
+ elif device.type == "mps":
+ print(f" ✓ Use {colored('CPU preprocessing', Style.GREEN)} (faster than GPU on MPS)")
+ print(f" ✓ {colored('SDPA', Style.GREEN)} provides moderate speedup")
+ else:
+ print(f" ✓ {colored('SDPA', Style.GREEN)} provides speedup over manual attention")
+ print(f" ○ Consider using GPU (CUDA/MPS) for better performance")
+
+
+# ============================================================================
+# MAIN
+# ============================================================================
+
+def main():
+ parser = argparse.ArgumentParser(
+ description="DA3 Full Benchmark - Test all optimization combinations",
+ formatter_class=argparse.RawDescriptionHelpFormatter,
+ epilog="""
+Examples:
+ python benchmarks/full_benchmark.py # Best device only
+ python benchmarks/full_benchmark.py -d all # All devices
+ python benchmarks/full_benchmark.py -d cuda # CUDA only
+ python benchmarks/full_benchmark.py --quick # Quick mode (fewer runs)
+ python benchmarks/full_benchmark.py --models da3-small da3-large
+ """
+ )
+ parser.add_argument("--quick", action="store_true", help="Quick mode (fewer runs)")
+ parser.add_argument("--skip-preprocessing", action="store_true", help="Skip preprocessing benchmark")
+ parser.add_argument("--skip-attention", action="store_true", help="Skip attention benchmark")
+ parser.add_argument("--skip-inference", action="store_true", help="Skip inference benchmark")
+ parser.add_argument("-d", "--device", type=str, default="auto",
+ choices=["auto", "cpu", "mps", "cuda", "all"],
+ help="Device to benchmark (default: auto)")
+ parser.add_argument("--models", nargs="+", default=None,
+ help="Models to benchmark (default: all)")
+ args = parser.parse_args()
+
+ # Configure runs
+ runs_preproc = 3 if args.quick else 5
+ runs_attn = 5 if args.quick else 10
+ runs_infer = 2 if args.quick else 4
+
+ # Determine models
+ if args.models:
+ models = args.models
+ elif args.quick:
+ models = ["da3-small", "da3-large"]
+ else:
+ models = ["da3-small", "da3-base", "da3-large"]
+
+ # Determine devices
+ available_devices = get_available_devices()
+ if args.device == "auto":
+ devices_to_test = [available_devices[-1]] # Best available
+ elif args.device == "all":
+ devices_to_test = available_devices
+ else:
+ requested = torch.device(args.device)
+ if requested in available_devices:
+ devices_to_test = [requested]
+ else:
+ print(f"Error: Device '{args.device}' not available.")
+ print(f"Available: {[d.type for d in available_devices]}")
+ return
+
+ # Main header
+ print()
+ print(colored("╔" + "═" * 68 + "╗", Style.CYAN))
+ print(colored("║", Style.CYAN) + colored(" DEPTH ANYTHING 3 - FULL BENCHMARK", Style.BOLD).center(77) + colored("║", Style.CYAN))
+ print(colored("║", Style.CYAN) + colored(" All Optimization Combinations", Style.DIM).center(77) + colored("║", Style.CYAN))
+ print(colored("╚" + "═" * 68 + "╝", Style.CYAN))
+
+ print(f"\n {Style.DIM}PyTorch{Style.RESET} : {colored(torch.__version__, Style.CYAN)}")
+ print(f" {Style.DIM}Models{Style.RESET} : {colored(', '.join(models), Style.CYAN)}")
+ print(f" {Style.DIM}Mode{Style.RESET} : {colored('Quick' if args.quick else 'Full', Style.CYAN)}")
+
+ print(f"\n {Style.DIM}Available devices:{Style.RESET}")
+ for d in available_devices:
+ status = colored("●", Style.GREEN) if d in devices_to_test else colored("○", Style.DIM)
+ print(f" {status} {d.type.upper():<6} {get_device_name(d)}")
+
+ all_results = {}
+
+ # Run benchmarks for each device
+ for device in devices_to_test:
+ device_name = get_device_name(device)
+ all_results[device.type] = {}
+
+ print_header(f"{device.type.upper()} - {device_name}")
+
+ # 1. Preprocessing
+ preproc_results = {}
+ if not args.skip_preprocessing and device.type != "cpu":
+ preproc_results = benchmark_preprocessing_detailed(device, runs=runs_preproc)
+ all_results[device.type]["preprocessing"] = preproc_results
+ print_preprocessing_results(preproc_results, device)
+ elif device.type == "cpu":
+ print_subheader("PREPROCESSING")
+ print(f" {Style.DIM}Skipped (CPU only - no GPU comparison){Style.RESET}")
+
+ # 2. Attention
+ attn_results = {}
+ if not args.skip_attention:
+ attn_results = benchmark_attention_detailed(device, runs=runs_attn)
+ all_results[device.type]["attention"] = attn_results
+ print_attention_results(attn_results, device)
+
+ # 3. Inference Matrix
+ infer_results = {}
+ if not args.skip_inference:
+ infer_results = benchmark_inference_matrix(device, models, runs=runs_infer)
+ all_results[device.type]["inference"] = infer_results
+ print_inference_matrix(infer_results, device)
+
+ # Device Summary
+ print_device_summary(device, preproc_results, attn_results, infer_results)
+
+ cleanup()
+
+ # Cross-device comparison
+ if len(devices_to_test) > 1 and not args.skip_inference:
+ print_header("CROSS-DEVICE COMPARISON")
+
+ # Find common model
+ common_model = models[-1] # Usually largest tested
+
+ print()
+ print(f" {colored(f'{common_model} (best config per device):', Style.CYAN)}")
+ print(f" {'Device':<10} {'Config':<30} {'Performance':<15}")
+ print(f" {'-'*55}")
+
+ base_fps = None
+ for device in devices_to_test:
+ if device.type in all_results and "inference" in all_results[device.type]:
+ infer = all_results[device.type]["inference"].get(common_model, {})
+ if infer:
+ best_name = max(infer.keys(), key=lambda k: infer[k]["result"].fps)
+ best = infer[best_name]
+ fps = best["result"].fps
+
+ if base_fps is None:
+ base_fps = fps
+
+ speedup = fps / base_fps if base_fps else 1
+ speedup_str = f"({speedup:.1f}x)" if device != devices_to_test[0] else "(baseline)"
+
+ print(f" {device.type.upper():<10} {best['config'].description:<30} {fps:>5.1f} img/s {speedup_str}")
+
+ # Final summary
+ print()
+ print(colored("═" * 70, Style.CYAN))
+ print(colored("║", Style.CYAN) + colored(" BENCHMARK COMPLETE", Style.BOLD).center(77) + colored("║", Style.CYAN))
+ print(colored("═" * 70, Style.CYAN))
+ print()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/benchmarks/gpu_preprocessing_benchmark.py b/benchmarks/gpu_preprocessing_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf513b5b58e7b8ab2d4e9bc1520dba08a19de4d7
--- /dev/null
+++ b/benchmarks/gpu_preprocessing_benchmark.py
@@ -0,0 +1,363 @@
+#!/usr/bin/env python3
+# Copyright (c) 2025 Delanoe Pirard
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+GPU Preprocessing Benchmark
+
+Compares CPU vs GPU preprocessing performance across different image sizes.
+Measures:
+- Preprocessing time only
+- Total inference time (preprocessing + model forward)
+- Memory usage
+- Speedup percentages
+"""
+
+import time
+from typing import List, Tuple
+
+import numpy as np
+import torch
+from PIL import Image
+
+from depth_anything_3.utils.io.input_processor import InputProcessor
+from depth_anything_3.utils.io.gpu_input_processor import GPUInputProcessor
+
+
+import os
+import shutil
+
+def create_test_files(sizes: List[Tuple[int, int]], count: int = 4, temp_dir: str = "temp_bench_imgs") -> Tuple[List[List[str]], str]:
+ """Create test image files on disk.
+
+ Args:
+ sizes: List of (width, height) tuples
+ count: Number of images per size
+ temp_dir: Directory to save images
+
+ Returns:
+ List of image path batches, one per size
+ Path to temp directory
+ """
+ if os.path.exists(temp_dir):
+ shutil.rmtree(temp_dir)
+ os.makedirs(temp_dir)
+
+ batches = []
+ for w, h, _ in sizes:
+ batch = []
+ for i in range(count):
+ img = Image.new("RGB", (w, h), color=(i * 50, 100, 150))
+ fname = f"{temp_dir}/{w}x{h}_{i}.jpg"
+ img.save(fname, quality=95, subsampling=0)
+ batch.append(fname)
+ batches.append(batch)
+ return batches, temp_dir
+
+def benchmark_gpu_decode_files(
+ processor,
+ image_paths: List[str],
+ process_res: int = 504,
+ warmup_runs: int = 2,
+ benchmark_runs: int = 10,
+ num_workers: int = 8,
+) -> float:
+ """Benchmark GPU decoding (from file path)."""
+ # Warmup
+ for _ in range(warmup_runs):
+ processor(
+ image=image_paths,
+ process_res=process_res,
+ process_res_method="upper_bound_resize",
+ num_workers=num_workers,
+ )
+
+ # Benchmark
+ times = []
+ for _ in range(benchmark_runs):
+ if hasattr(processor, 'device') and processor.device.type == "cuda":
+ torch.cuda.synchronize()
+
+ start = time.perf_counter()
+ # Pass file paths directly to GPUInputProcessor
+ tensor, _, _ = processor(
+ image=image_paths,
+ process_res=process_res,
+ process_res_method="upper_bound_resize",
+ num_workers=num_workers,
+ )
+
+ if hasattr(processor, 'device') and processor.device.type == "cuda":
+ torch.cuda.synchronize()
+
+ elapsed = time.perf_counter() - start
+ times.append(elapsed)
+
+ return np.mean(times)
+
+def create_test_images(sizes: List[Tuple[int, int]], count: int = 4) -> List[List[Image.Image]]:
+ """Create test images for each size.
+
+ Args:
+ sizes: List of (width, height) tuples
+ count: Number of images per size
+
+ Returns:
+ List of image batches, one per size
+ """
+ batches = []
+ for w, h in sizes:
+ batch = [Image.new("RGB", (w, h), color=(i * 50, 100, 150)) for i in range(count)]
+ batches.append(batch)
+ return batches
+
+
+def benchmark_hybrid(
+ processor,
+ images: List[Image.Image],
+ process_res: int = 504,
+ warmup_runs: int = 2,
+ benchmark_runs: int = 10,
+ num_workers: int = 8,
+ device=torch.device("cuda")
+) -> float:
+ """Benchmark hybrid preprocessing (CPU resize -> GPU normalize)."""
+
+ # Warmup
+ for _ in range(warmup_runs):
+ imgs_cpu, _, _ = processor(
+ image=images,
+ process_res=process_res,
+ process_res_method="upper_bound_resize",
+ num_workers=num_workers,
+ perform_normalization=False
+ )
+ imgs_gpu = imgs_cpu.to(device, non_blocking=True).float() / 255.0
+ _ = InputProcessor.normalize_tensor(imgs_gpu, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+ # Benchmark
+ times = []
+ for _ in range(benchmark_runs):
+ if device.type == "cuda":
+ torch.cuda.synchronize()
+
+ start = time.perf_counter()
+
+ # 1. CPU Preprocessing (uint8)
+ imgs_cpu, _, _ = processor(
+ image=images,
+ process_res=process_res,
+ process_res_method="upper_bound_resize",
+ num_workers=num_workers,
+ perform_normalization=False
+ )
+
+ # 2. Transfer + Normalize
+ imgs_gpu = imgs_cpu.to(device, non_blocking=True).float() / 255.0
+ _ = InputProcessor.normalize_tensor(imgs_gpu, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+ if device.type == "cuda":
+ torch.cuda.synchronize()
+
+ elapsed = time.perf_counter() - start
+ times.append(elapsed)
+
+ return np.mean(times)
+
+def benchmark_preprocessing(
+ processor,
+ images: List[Image.Image],
+ process_res: int = 504,
+ warmup_runs: int = 2,
+ benchmark_runs: int = 10,
+ num_workers: int = 8,
+) -> float:
+ """Benchmark preprocessing performance.
+
+ Args:
+ processor: InputProcessor or GPUInputProcessor instance
+ images: List of test images
+ process_res: Processing resolution
+ warmup_runs: Number of warmup runs to discard
+ benchmark_runs: Number of benchmark runs to average
+ num_workers: Number of parallel workers (for CPU processor)
+
+ Returns:
+ Average preprocessing time in seconds
+ """
+ # Warmup
+ for _ in range(warmup_runs):
+ processor(
+ image=images,
+ process_res=process_res,
+ process_res_method="upper_bound_resize",
+ num_workers=num_workers,
+ )
+
+ # Benchmark
+ times = []
+ for _ in range(benchmark_runs):
+ if hasattr(processor, 'device') and processor.device.type == "cuda":
+ torch.cuda.synchronize()
+
+ start = time.perf_counter()
+ tensor, _, _ = processor(
+ image=images,
+ process_res=process_res,
+ process_res_method="upper_bound_resize",
+ num_workers=num_workers,
+ )
+
+ if hasattr(processor, 'device') and processor.device.type == "cuda":
+ torch.cuda.synchronize()
+
+ elapsed = time.perf_counter() - start
+ times.append(elapsed)
+
+ return np.mean(times)
+
+
+def print_results_table(results: List[dict]):
+ """Pretty print benchmark results as table."""
+ print("\n" + "=" * 140)
+ print("GPU PREPROCESSING BENCHMARK RESULTS")
+ print("=" * 140)
+ print(f"{'Image Size':<15} {'CPU Time':<12} {'GPU Time':<12} {'Hybrid Time':<12} {'GPU Decode':<12} {'Best Method':<15}")
+ print("-" * 140)
+
+ for result in results:
+ size_str = f"{result['width']}x{result['height']}"
+ cpu_time = f"{result['cpu_time']*1000:.2f} ms"
+ gpu_time = f"{result['gpu_time']*1000:.2f} ms"
+ hybrid_time = f"{result['hybrid_time']*1000:.2f} ms"
+ gpu_decode_time = f"{result['gpu_decode_time']*1000:.2f} ms"
+
+ times = [result['cpu_time'], result['gpu_time'], result['hybrid_time'], result['gpu_decode_time']]
+ labels = ["CPU", "GPU", "Hybrid", "GPU Decode"]
+ best_idx = np.argmin(times)
+ best = labels[best_idx]
+
+ print(f"{size_str:<15} {cpu_time:<12} {gpu_time:<12} {hybrid_time:<12} {gpu_decode_time:<12} {best:<15}")
+
+ print("=" * 140 + "\n")
+
+
+def main():
+ """Run comprehensive benchmark."""
+ print("\n" + "=" * 100)
+ print("INITIALIZING GPU PREPROCESSING BENCHMARK")
+ print("=" * 100)
+
+ # Check GPU availability
+ if torch.cuda.is_available():
+ device_name = "cuda"
+ device_info = torch.cuda.get_device_name(0)
+ print(f"✓ GPU Device: {device_info}")
+ print("✓ GPU preprocessing: ENABLED (NVJPEG + Kornia)")
+ elif torch.backends.mps.is_available():
+ device_name = "mps"
+ device_info = "Apple MPS"
+ print(f"✓ GPU Device: {device_info}")
+ print("ℹ GPU preprocessing: DISABLED on MPS (CPU is faster on Apple Silicon)")
+ print(" → GPUInputProcessor will use CPU path automatically")
+ print(" → GPU reserved for model inference (5-10x speedup there)")
+ else:
+ print("✗ No GPU available - benchmark will show CPU vs CPU (no speedup expected)")
+ device_name = "cpu"
+ device_info = "CPU only"
+
+ device = torch.device(device_name)
+
+ # Create processors
+ cpu_proc = InputProcessor()
+ gpu_proc = GPUInputProcessor(device=device_name)
+ print(f"✓ Processors initialized: CPU vs {device_name.upper()}")
+
+ # Test configurations
+ # Format: (width, height, description)
+ test_sizes = [
+ (640, 480, "Small (VGA)"),
+ (1280, 720, "Medium (HD)"),
+ (1920, 1080, "Large (Full HD)"),
+ (3840, 2160, "XLarge (4K)"),
+ ]
+
+ process_res = 504
+ num_images = 4
+ num_workers = 8
+
+ print(f"✓ Test config: {num_images} images per batch, process_res={process_res}, num_workers={num_workers}")
+ print(f"✓ Testing {len(test_sizes)} image sizes: {', '.join([desc for _, _, desc in test_sizes])}")
+
+ # Create test images
+ print("\nGenerating test images (PIL & Files)...")
+ image_batches_pil = create_test_images([(w, h) for w, h, _ in test_sizes], count=num_images)
+ image_batches_files, temp_dir = create_test_files(test_sizes, count=num_images)
+ print("✓ Test images generated")
+
+ # Run benchmarks
+ print("\nRunning benchmarks (this may take a minute)...\n")
+ results = []
+
+ try:
+ for (w, h, desc), imgs_pil, imgs_files in zip(test_sizes, image_batches_pil, image_batches_files):
+ print(f"Benchmarking {desc} ({w}x{h})...", end=" ", flush=True)
+
+ cpu_time = benchmark_preprocessing(cpu_proc, imgs_pil, process_res, num_workers=num_workers)
+ gpu_time = benchmark_preprocessing(gpu_proc, imgs_pil, process_res, num_workers=num_workers)
+ hybrid_time = benchmark_hybrid(cpu_proc, imgs_pil, process_res, num_workers=num_workers, device=device)
+
+ # GPU Decode uses file paths
+ gpu_decode_time = benchmark_gpu_decode_files(gpu_proc, imgs_files, process_res, num_workers=num_workers)
+
+ results.append({
+ 'width': w,
+ 'height': h,
+ 'description': desc,
+ 'cpu_time': cpu_time,
+ 'gpu_time': gpu_time,
+ 'hybrid_time': hybrid_time,
+ 'gpu_decode_time': gpu_decode_time
+ })
+
+ best_time = min(cpu_time, gpu_time, hybrid_time, gpu_decode_time)
+ if best_time == gpu_decode_time:
+ win = "GPU Decode"
+ elif best_time == hybrid_time:
+ win = "Hybrid"
+ elif best_time == gpu_time:
+ win = "GPU"
+ else:
+ win = "CPU"
+
+ print(f"✓ Best: {win}")
+
+ # Print results table
+ print_results_table(results)
+
+ # Memory info (CUDA only)
+ if device_name == "cuda":
+ print("\nGPU Memory Usage:")
+ print(f" Allocated: {torch.cuda.memory_allocated(0) / 1024**2:.1f} MB")
+ print(f" Cached: {torch.cuda.memory_reserved(0) / 1024**2:.1f} MB")
+
+ finally:
+ # Cleanup
+ if os.path.exists(temp_dir):
+ shutil.rmtree(temp_dir)
+ print(f"\n✓ Cleaned up temp directory: {temp_dir}")
+
+if __name__ == "__main__":
+ main()
+
diff --git a/benchmarks/results/temp_images/test_image_0000.jpg b/benchmarks/results/temp_images/test_image_0000.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c94f7a8d014884544d958dd0abc0621ec2199fbd
--- /dev/null
+++ b/benchmarks/results/temp_images/test_image_0000.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b66fe582ae8d3bb62f6dcff97784125ae59ea7e62bdd49e89699fb7664c41a08
+size 360371
diff --git a/benchmarks/results/temp_images/test_image_0001.jpg b/benchmarks/results/temp_images/test_image_0001.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..45689743b02d613569bd055731e2b4f78bf86e37
--- /dev/null
+++ b/benchmarks/results/temp_images/test_image_0001.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:24b4ecf7b65cc1f6c85453a03417da5a48d23295824d275e0cc9361a4feb36c5
+size 360564
diff --git a/benchmarks/results/temp_images/test_image_0002.jpg b/benchmarks/results/temp_images/test_image_0002.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e6beaa8d0a80eaa64610897d54a6e48d05407387
--- /dev/null
+++ b/benchmarks/results/temp_images/test_image_0002.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1dc218e581801d2332d2b7fe4eb35a997dc8d564d643dadeb2ee7539c7bf76f9
+size 360218
diff --git a/benchmarks/results/temp_images/test_image_0003.jpg b/benchmarks/results/temp_images/test_image_0003.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..544e3d9f4cc32086cc70e36d8a3be2f45d8579e3
--- /dev/null
+++ b/benchmarks/results/temp_images/test_image_0003.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4749f606ec40b7385eda5edecf5be20d02caed202fca5d39e198765dc93557f7
+size 360306
diff --git a/docs/API.md b/docs/API.md
new file mode 100644
index 0000000000000000000000000000000000000000..89a9de362ce260b37cc86c6345974ef372ad9db9
--- /dev/null
+++ b/docs/API.md
@@ -0,0 +1,465 @@
+# 📚 DepthAnything3 API Documentation
+
+## 📑 Table of Contents
+
+1. [📖 Overview](#overview)
+2. [💡 Usage Examples](#usage-examples)
+3. [🔧 Core API](#core-api)
+ - [DepthAnything3 Class](#depthanything3-class)
+ - [inference() Method](#inference-method)
+4. [⚙️ Parameters](#parameters)
+ - [Input Parameters](#input-parameters)
+ - [Pose Alignment Parameters](#pose-alignment-parameters)
+ - [Feature Export Parameters](#feature-export-parameters)
+ - [Rendering Parameters](#rendering-parameters)
+ - [Processing Parameters](#processing-parameters)
+ - [Export Parameters](#export-parameters)
+5. [📤 Export Formats](#export-formats)
+6. [↩️ Return Value](#return-value)
+
+## 📖 Overview
+
+This documentation provides comprehensive API reference for DepthAnything3, including usage examples, parameter specifications, export formats, and advanced features. It covers both basic pose and depth estimation workflows and advanced pose-conditioned processing with multiple export capabilities.
+
+## 💡 Usage Examples
+
+Here are quick examples to get you started:
+
+### 🚀 Basic Depth Estimation
+```python
+from depth_anything_3.api import DepthAnything3
+
+# Initialize and run inference
+model = DepthAnything3.from_pretrained("depth-anything/DA3NESTED-GIANT-LARGE").to("cuda")
+prediction = model.inference(["image1.jpg", "image2.jpg"])
+```
+
+### 📷 Pose-Conditioned Depth Estimation
+```python
+import numpy as np
+
+# With camera parameters for better consistency
+prediction = model.inference(
+ image=["image1.jpg", "image2.jpg"],
+ extrinsics=extrinsics_array, # (N, 4, 4)
+ intrinsics=intrinsics_array # (N, 3, 3)
+)
+```
+
+### 📤 Export Results
+```python
+# Export depth data and 3D visualization
+prediction = model.inference(
+ image=image_paths,
+ export_dir="./output",
+ export_format="mini_npz-glb"
+)
+```
+
+### 🔍 Feature Extraction
+```python
+# Export intermediate features from specific layers
+prediction = model.inference(
+ image=image_paths,
+ export_dir="./output",
+ export_format="feat_vis",
+ export_feat_layers=[0, 1, 2] # Export features from layers 0, 1, 2
+)
+```
+
+### ✨ Advanced Export with Gaussian Splatting
+```python
+# Export multiple formats including Gaussian Splatting
+# Note: infer_gs=True requires da3-giant or da3nested-giant-large model
+model = DepthAnything3(model_name="da3-giant").to("cuda")
+
+prediction = model.inference(
+ image=image_paths,
+ extrinsics=extrinsics_array,
+ intrinsics=intrinsics_array,
+ export_dir="./output",
+ export_format="npz-glb-gs_ply-gs_video",
+ align_to_input_ext_scale=True,
+ infer_gs=True, # Required for gs_ply and gs_video exports
+)
+```
+
+### 🎨 Advanced Export with Feature Visualization
+```python
+# Export with intermediate feature visualization
+prediction = model.inference(
+ image=image_paths,
+ export_dir="./output",
+ export_format="mini_npz-glb-depth_vis-feat_vis",
+ export_feat_layers=[0, 5, 10, 15, 20],
+ feat_vis_fps=30,
+)
+```
+
+### 📐 Using Ray-Based Pose Estimation
+```python
+# Use ray-based pose estimation instead of camera decoder
+prediction = model.inference(
+ image=image_paths,
+ export_dir="./output",
+ export_format="glb",
+ use_ray_pose=True, # Enable ray-based pose estimation
+)
+```
+
+### 🎯 Reference View Selection
+```python
+# For multi-view inputs, automatically select the best reference view
+prediction = model.inference(
+ image=image_paths,
+ ref_view_strategy="saddle_balanced", # Default: balanced selection
+)
+
+# For video sequences, use middle frame as reference
+prediction = model.inference(
+ image=video_frames,
+ ref_view_strategy="middle", # Good for temporally ordered inputs
+)
+```
+
+## 🔧 Core API
+
+### 🔨 DepthAnything3 Class
+
+The main API class that provides depth estimation capabilities with optional pose conditioning.
+
+#### 🎯 Initialization
+
+```python
+from depth_anything_3 import DepthAnything3
+
+# Initialize the model with a model name
+model = DepthAnything3(model_name="da3-large")
+model = model.to("cuda") # Move to GPU
+```
+
+**Parameters:**
+- `model_name` (str, default: "da3-large"): The name of the model preset to use.
+ - **Available models:**
+ - 🦾 `"da3-giant"` - 1.15B params, any-view model with GS support
+ - ⭐ `"da3-large"` - 0.35B params, any-view model (recommended for most use cases)
+ - 📦 `"da3-base"` - 0.12B params, any-view model
+ - 🪶 `"da3-small"` - 0.08B params, any-view model
+ - 👁️ `"da3mono-large"` - 0.35B params, monocular depth only
+ - 📏 `"da3metric-large"` - 0.35B params, metric depth with sky segmentation
+ - 🎯 `"da3nested-giant-large"` - 1.40B params, nested model with all features
+
+### 🚀 inference() Method
+
+The primary inference method that processes images and returns depth predictions.
+
+```python
+prediction = model.inference(
+ image=image_list,
+ extrinsics=extrinsics_array, # Optional
+ intrinsics=intrinsics_array, # Optional
+ align_to_input_ext_scale=True, # Whether to align predicted poses to input scale
+ infer_gs=True, # Enable Gaussian branch for gs exports
+ use_ray_pose=False, # Use ray-based pose estimation instead of camera decoder
+ ref_view_strategy="saddle_balanced", # Reference view selection strategy
+ render_exts=render_extrinsics, # Optional renders for gs_video
+ render_ixts=render_intrinsics, # Optional renders for gs_video
+ render_hw=(height, width), # Optional renders for gs_video
+ process_res=504,
+ process_res_method="upper_bound_resize",
+ export_dir="output_directory", # Optional
+ export_format="mini_npz",
+ export_feat_layers=[], # List of layer indices to export features from
+ conf_thresh_percentile=40.0, # Confidence threshold percentile for depth map in GLB export
+ num_max_points=1_000_000, # Maximum number of points to export in GLB export
+ show_cameras=True, # Whether to show cameras in GLB export
+ feat_vis_fps=15, # Frames per second for feature visualization in feat_vis export
+ export_kwargs={} # Optional, additional arguments to export functions. export_format:key:val, see 'Parameters/Export Parameters' for details
+)
+```
+
+## ⚙️ Parameters
+
+### 📸 Input Parameters
+
+#### `image` (required)
+- **Type**: `List[Union[np.ndarray, Image.Image, str]]`
+- **Description**: List of input images. Can be numpy arrays, PIL Images, or file paths.
+- **Example**:
+ ```python
+ # From file paths
+ image = ["image1.jpg", "image2.jpg", "image3.jpg"]
+
+ # From numpy arrays
+ image = [np.array(img1), np.array(img2)]
+
+ # From PIL Images
+ image = [Image.open("image1.jpg"), Image.open("image2.jpg")]
+ ```
+
+#### `extrinsics` (optional)
+- **Type**: `Optional[np.ndarray]`
+- **Shape**: `(N, 4, 4)` where N is the number of input images
+- **Description**: Camera extrinsic matrices (world-to-camera transformation). When provided, enables pose-conditioned depth estimation mode.
+- **Note**: If not provided, the model operates in standard depth estimation mode.
+
+#### `intrinsics` (optional)
+- **Type**: `Optional[np.ndarray]`
+- **Shape**: `(N, 3, 3)` where N is the number of input images
+- **Description**: Camera intrinsic matrices containing focal length and principal point information. When provided, enables pose-conditioned depth estimation mode.
+
+### 🎯 Pose Alignment Parameters
+
+#### `align_to_input_ext_scale` (default: True)
+- **Type**: `bool`
+- **Description**: When True the predicted extrinsics are replaced with the input
+ ones and the depth maps are rescaled to match their metric scale. When False the
+ function returns the internally aligned poses computed via Umeyama alignment.
+
+#### `infer_gs` (default: False)
+- **Type**: `bool`
+- **Description**: Enable Gaussian Splatting branch for gaussian splatting exports. Required when using `gs_ply` or `gs_video` export formats.
+
+#### `use_ray_pose` (default: False)
+- **Type**: `bool`
+- **Description**: Use ray-based pose estimation instead of camera decoder for pose prediction. When True, the model uses ray prediction heads to estimate camera poses; when False, it uses the camera decoder approach.
+
+#### `ref_view_strategy` (default: "saddle_balanced")
+- **Type**: `str`
+- **Description**: Strategy for selecting the reference view from multiple input views. Options: `"first"`, `"middle"`, `"saddle_balanced"`, `"saddle_sim_range"`. Only applied when number of views ≥ 3. See [detailed documentation](funcs/ref_view_strategy.md) for strategy comparisons.
+- **Available strategies**:
+ - `"saddle_balanced"`: Selects view with balanced features across multiple metrics (recommended default)
+ - `"saddle_sim_range"`: Selects view with largest similarity range
+ - `"first"`: Always uses first view (not recommended, equivalent to no reordering for views < 3)
+ - `"middle"`: Uses middle view (recommended for video sequences)
+
+### 🔍 Feature Export Parameters
+
+#### `export_feat_layers` (default: [])
+- **Type**: `List[int]`
+- **Description**: List of layer indices to export intermediate features from. Features are stored in the `aux` dictionary of the Prediction object with keys like `feat_layer_0`, `feat_layer_1`, etc.
+
+### 🎥 Rendering Parameters
+
+These arguments are only used when exporting Gaussian-splatting videos (include
+`"gs_video"` in `export_format`). They describe an auxiliary camera trajectory
+with ``M`` views.
+
+#### `render_exts` (optional)
+- **Type**: `Optional[np.ndarray]`
+- **Shape**: `(M, 4, 4)`
+- **Description**: Camera extrinsics for the synthesized trajectory. If omitted,
+ the exporter falls back to the predicted poses.
+
+#### `render_ixts` (optional)
+- **Type**: `Optional[np.ndarray]`
+- **Shape**: `(M, 3, 3)`
+- **Description**: Camera intrinsics for each rendered frame. Leave `None` to
+ reuse the input intrinsics.
+
+#### `render_hw` (optional)
+- **Type**: `Optional[Tuple[int, int]]`
+- **Description**: Explicit output resolution `(height, width)` for the rendered
+ frames. Defaults to the input resolution when not provided.
+
+### ⚡ Processing Parameters
+
+#### `process_res` (default: 504)
+- **Type**: `int`
+- **Description**: Base resolution for processing. The model will resize images to this resolution for inference.
+
+#### `process_res_method` (default: "upper_bound_resize")
+- **Type**: `str`
+- **Description**: Method for resizing images to the target resolution.
+- **Options**:
+ - `"upper_bound_resize"`: Resize so that the specified dimension (504) becomes the longer side
+ - `"lower_bound_resize"`: Resize so that the specified dimension (504) becomes the shorter side
+- **Example**:
+ - Input: 1200×1600 → Output: 378×504 (with `process_res=504`, `process_res_method="upper_bound_resize"`)
+ - Input: 504×672 → Output: 504×672 (no change needed)
+
+### 📦 Export Parameters
+
+#### `export_dir` (optional)
+- **Type**: `Optional[str]`
+- **Description**: Directory path where exported files will be saved. If not provided, no files will be exported.
+
+#### `export_format` (default: "mini_npz")
+- **Type**: `str`
+- **Description**: Format for exporting results. Supports multiple formats separated by `-`.
+- **Example**: `"mini_npz-glb"` exports both mini_npz and glb formats.
+
+#### 🌐 GLB Export Parameters
+
+These parameters are passed directly to the `inference()` method and only apply when `export_format` includes `"glb"`.
+
+##### `conf_thresh_percentile` (default: 40.0)
+- **Type**: `float`
+- **Description**: Lower percentile for adaptive confidence threshold. Points below this confidence percentile will be filtered out from the point cloud.
+
+##### `num_max_points` (default: 1,000,000)
+- **Type**: `int`
+- **Description**: Maximum number of points in the exported point cloud. If the point cloud exceeds this limit, it will be downsampled.
+
+##### `show_cameras` (default: True)
+- **Type**: `bool`
+- **Description**: Whether to include camera wireframes in the exported GLB file for visualization.
+
+#### 🎨 Feature Visualization Parameters
+
+These parameters are passed directly to the `inference()` method and only apply when `export_format` includes `"feat_vis"`.
+
+##### `feat_vis_fps` (default: 15)
+- **Type**: `int`
+- **Description**: Frame rate for the output video when visualizing features across multiple images.
+
+#### ✨🎥 3DGS and 3DGS Video Parameters
+
+These parameters are passed directly to the `inference()` method and only apply when `export_format` includes `"gs_ply"` or `"gs_video"`.
+
+##### `export_kwargs` (default: `{}`)
+- Type: `dict[str, dict[str, Any]]`
+- Description: Per-format extra arguments passed to export functions, mainly for `"gs_ply"` and `"gs_video"`.
+ - Access pattern: `export_kwargs[export_format][key] = value`
+ - Example:
+ ```python
+ {
+ "gs_ply": {
+ "gs_views_interval": 1,
+ },
+ "gs_video": {
+ "trj_mode": "interpolate_smooth",
+ "chunk_size": 1,
+ "vis_depth": None,
+ },
+ }
+ ```
+
+## 📤 Export Formats
+
+The API supports multiple export formats for different use cases:
+
+### 📊 `mini_npz`
+- **Description**: Minimal NPZ format containing essential data
+- **Contents**: `depth`, `conf`, `exts`, `ixts`
+- **Use case**: Lightweight storage for depth data with camera parameters
+
+### 📦 `npz`
+- **Description**: Full NPZ format with comprehensive data
+- **Contents**: `depth`, `conf`, `exts`, `ixts`, `image`, etc.
+- **Use case**: Complete data export for advanced processing
+
+### 🌐 `glb`
+- **Description**: 3D visualization format with point cloud and camera poses
+- **Contents**:
+ - Point cloud with colors from original images
+ - Camera wireframes for visualization
+ - Confidence-based filtering and downsampling
+- **Use case**: 3D visualization, inspection, and analysis
+- **Features**:
+ - Automatic sky depth handling
+ - Confidence threshold filtering
+ - Background filtering (black/white)
+ - Scene scale normalization
+- **Parameters** (passed via `inference()` method directly):
+ - `conf_thresh_percentile` (float, default: 40.0): Lower percentile for adaptive confidence threshold. Points below this confidence percentile will be filtered out.
+ - `num_max_points` (int, default: 1,000,000): Maximum number of points in the exported point cloud. If exceeded, points will be downsampled.
+ - `show_cameras` (bool, default: True): Whether to include camera wireframes in the exported GLB file for visualization.
+
+### ✨ `gs_ply`
+- **Description**: Gaussian Splatting point cloud format
+- **Contents**: 3DGS data in PLY format. Compatible with standard 3DGS viewers such as [SuperSplat](https://superspl.at/editor) (recommended), [SPARK](https://sparkjs.dev/viewer/).
+- **Use case**: Gaussian Splatting reconstruction
+- **Requirements**: Must set `infer_gs=True` when calling `inference()`. Only supported by `da3-giant` and `da3nested-giant-large` models.
+- **Additional configs**, provided via `export_kwargs` (see [Export Parameters](#export-parameters)):
+ - `gs_views_interval`: Export to 3DGS every N views, default: `1`.
+
+### 🎥 `gs_video`
+- **Description**: Rasterized 3DGS to obtain videos
+- **Contents**: A video of 3DGS-rasterized views using either provided viewpoints or a predefined camera trajectory.
+- **Use case**: Video rendering for Gaussian Splatting
+- **Requirements**: Must set `infer_gs=True` when calling `inference()`. Only supported by `da3-giant` and `da3nested-giant-large` models.
+- **Note**: Can optionally use `render_exts`, `render_ixts`, and `render_hw` parameters in `inference()` method to specify novel viewpoints.
+- **Additional configs**, provided via `export_kwargs` (see [Export Parameters](#export-parameters)):
+ - `extrinsics`: Optional world-to-camera poses for novel views. Falls back to the predicted poses of input views if not provided. (Alternatively, use `render_exts` parameter in `inference()`)
+ - `intrinsics`: Optional camera intrinsics for novel views. Falls back to the predicted intrinsics of input views if not provided. (Alternatively, use `render_ixts` parameter in `inference()`)
+ - `out_image_hw`: Optional output resolution `H x W`. Falls back to input resolution if not provided. (Alternatively, use `render_hw` parameter in `inference()`)
+ - `chunk_size`: Number of views rasterized per batch. Default: `8`.
+ - `trj_mode`: Predefined camera trajectory for novel-view rendering.
+ - `color_mode`: Same as `render_mode` in [gsplat](https://docs.gsplat.studio/main/apis/rasterization.html#gsplat.rasterization).
+ - `vis_depth`: How depth is combined with RGB. Default: `hcat` (horizontal concatenation).
+ - `enable_tqdm`: Whether to display a tqdm progress bar during rendering.
+ - `output_name`: File name of the rendered video.
+ - `video_quality`: Video quality to save. Default: `high`.
+ - `high`: High quality video (default)
+ - `medium`: Medium quality video (balance of storage space and quality)
+ - `low`: Low quality video (fewer storage space)
+
+### 🔍 `feat_vis`
+- **Description**: Feature visualization format
+- **Contents**: PCA-visualized intermediate features from specified layers
+- **Use case**: Model interpretability and feature analysis
+- **Note**: Requires `export_feat_layers` to be specified
+- **Parameters** (passed via `inference()` method directly):
+ - `feat_vis_fps` (int, default: 15): Frame rate for the output video when visualizing features across multiple images.
+
+### 🎨 `depth_vis`
+- **Description**: Depth visualization format
+- **Contents**: Color-coded depth maps alongside original images
+- **Use case**: Visual inspection of depth estimation quality
+
+### 🔗 Multiple Format Export
+You can export multiple formats simultaneously by separating them with `-`:
+
+```python
+# Export both mini_npz and glb formats
+export_format = "mini_npz-glb"
+
+# Export multiple formats
+export_format = "npz-glb-gs_ply"
+```
+
+## ↩️ Return Value
+
+The `inference()` method returns a `Prediction` object with the following attributes:
+
+### 📊 Core Outputs
+
+- **depth**: `np.ndarray` - Estimated depth maps with shape `(N, H, W)` where N is the number of images, H is height, and W is width.
+- **conf**: `np.ndarray` - Confidence maps with shape `(N, H, W)` indicating prediction reliability (optional, depends on model).
+
+### 📷 Camera Parameters
+
+- **extrinsics**: `np.ndarray` - Camera extrinsic matrices with shape `(N, 3, 4)` representing world-to-camera transformations. Only present if camera poses were estimated or provided as input.
+- **intrinsics**: `np.ndarray` - Camera intrinsic matrices with shape `(N, 3, 3)` containing focal length and principal point information. Only present if poses were estimated or provided as input.
+
+### 🎁 Additional Outputs
+
+- **processed_images**: `np.ndarray` - Preprocessed input images with shape `(N, H, W, 3)` in RGB format (0-255 uint8).
+- **aux**: `dict` - Auxiliary outputs including:
+ - `feat_layer_X`: Intermediate features from layer X (if `export_feat_layers` was specified)
+ - `gaussians`: 3D Gaussian Splats data (if `infer_gs=True`)
+
+### 💻 Usage Example
+
+```python
+prediction = model.inference(image=["img1.jpg", "img2.jpg"])
+
+# Access depth maps
+depth_maps = prediction.depth # shape: (2, H, W)
+
+# Access confidence
+if hasattr(prediction, 'conf'):
+ confidence = prediction.conf
+
+# Access camera parameters (if available)
+if hasattr(prediction, 'extrinsics'):
+ camera_poses = prediction.extrinsics # shape: (2, 4, 4)
+
+if hasattr(prediction, 'intrinsics'):
+ camera_intrinsics = prediction.intrinsics # shape: (2, 3, 3)
+
+# Access intermediate features (if export_feat_layers was set)
+if hasattr(prediction, 'aux') and 'feat_layer_0' in prediction.aux:
+ features = prediction.aux['feat_layer_0']
+```
diff --git a/docs/CLI.md b/docs/CLI.md
new file mode 100644
index 0000000000000000000000000000000000000000..c99ba0bb7540028bae3e0fbe93c0945fe5140c1e
--- /dev/null
+++ b/docs/CLI.md
@@ -0,0 +1,654 @@
+# 🚀 Depth Anything 3 Command Line Interface
+
+## 📋 Table of Contents
+
+- [📖 Overview](#overview)
+- [⚡ Quick Start](#quick-start)
+- [📚 Command Reference](#command-reference)
+ - [🤖 auto - Auto Mode](#auto---auto-mode)
+ - [🖼️ image - Single Image Processing](#image---single-image-processing)
+ - [🗂️ images - Image Directory Processing](#images---image-directory-processing)
+ - [🎬 video - Video Processing](#video---video-processing)
+ - [📐 colmap - COLMAP Dataset Processing](#colmap---colmap-dataset-processing)
+ - [🔧 backend - Backend Service](#backend---backend-service)
+ - [🎨 gradio - Gradio Application](#gradio---gradio-application)
+ - [🖼️ gallery - Gallery Server](#gallery---gallery-server)
+- [⚙️ Parameter Details](#parameter-details)
+- [💡 Usage Examples](#usage-examples)
+
+## 📖 Overview
+
+The Depth Anything 3 CLI provides a comprehensive command-line toolkit supporting image depth estimation, video processing, COLMAP dataset handling, and web applications.
+
+The backend service enables cache model to GPU so that we do not need to reload model for each command.
+
+## ⚡ Quick Start
+
+The CLI can run fully offline or connect to the backend for cached weights and task scheduling:
+
+```bash
+# 🔧 Start backend service (optional, keeps model resident in GPU memory)
+da3 backend --model-dir depth-anything/DA3NESTED-GIANT-LARGE
+
+# 🚀 Use auto mode to process input
+da3 auto path/to/input --export-dir ./workspace/scene001
+
+# ♻️ Reuse backend for next job
+da3 auto path/to/video.mp4 \
+ --export-dir ./workspace/scene002 \
+ --use-backend \
+ --backend-url http://localhost:8008
+```
+
+Each export directory contains `scene.glb`, `scene.jpg`, and optional extras such as `depth_vis/` or `gs_video/` depending on the requested format.
+
+## 📚 Command Reference
+
+### 🤖 auto - Auto Mode
+
+Automatically detect input type and dispatch to the appropriate handler.
+
+**Usage:**
+
+```bash
+da3 auto INPUT_PATH [OPTIONS]
+```
+
+**Input Type Detection:**
+- 🖼️ Single image file (.jpg, .png, .jpeg, .webp, .bmp, .tiff, .tif)
+- 📁 Image directory
+- 🎬 Video file (.mp4, .avi, .mov, .mkv, .flv, .wmv, .webm, .m4v)
+- 📐 COLMAP directory (containing `images/` and `sparse/` subdirectories)
+
+**Parameters:**
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `INPUT_PATH` | str | Required | Input path (image, directory, video, or COLMAP) |
+| `--model-dir` | str | Default model | Model directory path |
+| `--export-dir` | str | `debug` | Export directory |
+| `--export-format` | str | `glb` | Export format (supports `mini_npz`, `glb`, `feat_vis`, etc., can be combined with hyphens) |
+| `--device` | str | `cuda` | Device to use |
+| `--use-backend` | bool | `False` | Use backend service for inference |
+| `--backend-url` | str | `http://localhost:8008` | Backend service URL |
+| `--process-res` | int | `504` | Processing resolution |
+| `--process-res-method` | str | `upper_bound_resize` | Processing resolution method |
+| `--export-feat` | str | `""` | Export features from specified layers, comma-separated (e.g., `"0,1,2"`) |
+| `--auto-cleanup` | bool | `False` | Automatically clean export directory without confirmation |
+| `--fps` | float | `1.0` | [Video] Frame sampling FPS |
+| `--sparse-subdir` | str | `""` | [COLMAP] Sparse reconstruction subdirectory (e.g., `"0"` for `sparse/0/`) |
+| `--align-to-input-ext-scale` | bool | `True` | [COLMAP] Align prediction to input extrinsics scale |
+| `--use-ray-pose` | bool | `False` | Use ray-based pose estimation instead of camera decoder |
+| `--ref-view-strategy` | str | `saddle_balanced` | Reference view selection strategy: `first`, `middle`, `saddle_balanced`, `saddle_sim_range`. See [docs](funcs/ref_view_strategy.md) |
+| `--conf-thresh-percentile` | float | `40.0` | [GLB] Lower percentile for adaptive confidence threshold |
+| `--num-max-points` | int | `1000000` | [GLB] Maximum number of points in the point cloud |
+| `--show-cameras` | bool | `True` | [GLB] Show camera wireframes in the exported scene |
+| `--feat-vis-fps` | int | `15` | [FEAT_VIS] Frame rate for output video |
+
+**Examples:**
+
+```bash
+# 🖼️ Auto-process an image
+da3 auto path/to/image.jpg --export-dir ./output
+
+# 🎬 Auto-process a video
+da3 auto path/to/video.mp4 --fps 2.0 --export-dir ./output
+
+# 🔧 Use backend service
+da3 auto path/to/input \
+ --export-format mini_npz-glb \
+ --use-backend \
+ --backend-url http://localhost:8008 \
+ --export-dir ./output
+```
+
+---
+
+### 🖼️ image - Single Image Processing
+
+Process a single image for camera pose and depth estimation.
+
+**Usage:**
+
+```bash
+da3 image IMAGE_PATH [OPTIONS]
+```
+
+**Parameters:**
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `IMAGE_PATH` | str | Required | Input image file path |
+| `--model-dir` | str | Default model | Model directory path |
+| `--export-dir` | str | `debug` | Export directory |
+| `--export-format` | str | `glb` | Export format |
+| `--device` | str | `cuda` | Device to use |
+| `--use-backend` | bool | `False` | Use backend service for inference |
+| `--backend-url` | str | `http://localhost:8008` | Backend service URL |
+| `--process-res` | int | `504` | Processing resolution |
+| `--process-res-method` | str | `upper_bound_resize` | Processing resolution method |
+| `--export-feat` | str | `""` | Export feature layer indices (comma-separated) |
+| `--auto-cleanup` | bool | `False` | Automatically clean export directory |
+| `--use-ray-pose` | bool | `False` | Use ray-based pose estimation instead of camera decoder |
+| `--ref-view-strategy` | str | `saddle_balanced` | Reference view selection strategy. See [docs](funcs/ref_view_strategy.md) |
+| `--conf-thresh-percentile` | float | `40.0` | [GLB] Confidence threshold percentile |
+| `--num-max-points` | int | `1000000` | [GLB] Maximum number of points |
+| `--show-cameras` | bool | `True` | [GLB] Show cameras |
+| `--feat-vis-fps` | int | `15` | [FEAT_VIS] Video frame rate |
+
+**Examples:**
+
+```bash
+# ✨ Basic usage
+da3 image path/to/image.png --export-dir ./output
+
+# ⚡ With backend acceleration
+da3 image path/to/image.png \
+ --use-backend \
+ --backend-url http://localhost:8008 \
+ --export-dir ./output
+
+# 🔍 Export feature visualization
+da3 image image.jpg \
+ --export-format feat_vis \
+ --export-feat "9,19,29,39" \
+ --export-dir ./results
+```
+
+---
+
+### 🗂️ images - Image Directory Processing
+
+Process a directory of images for batch depth estimation.
+
+**Usage:**
+
+```bash
+da3 images IMAGES_DIR [OPTIONS]
+```
+
+**Parameters:**
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `IMAGES_DIR` | str | Required | Directory path containing images |
+| `--image-extensions` | str | `png,jpg,jpeg` | Image file extensions to process (comma-separated) |
+| `--model-dir` | str | Default model | Model directory path |
+| `--export-dir` | str | `debug` | Export directory |
+| `--export-format` | str | `glb` | Export format |
+| `--device` | str | `cuda` | Device to use |
+| `--use-backend` | bool | `False` | Use backend service for inference |
+| `--backend-url` | str | `http://localhost:8008` | Backend service URL |
+| `--process-res` | int | `504` | Processing resolution |
+| `--process-res-method` | str | `upper_bound_resize` | Processing resolution method |
+| `--export-feat` | str | `""` | Export feature layer indices |
+| `--auto-cleanup` | bool | `False` | Automatically clean export directory |
+| `--use-ray-pose` | bool | `False` | Use ray-based pose estimation instead of camera decoder |
+| `--ref-view-strategy` | str | `saddle_balanced` | Reference view selection strategy. See [docs](funcs/ref_view_strategy.md) |
+| `--conf-thresh-percentile` | float | `40.0` | [GLB] Confidence threshold percentile |
+| `--num-max-points` | int | `1000000` | [GLB] Maximum number of points |
+| `--show-cameras` | bool | `True` | [GLB] Show cameras |
+| `--feat-vis-fps` | int | `15` | [FEAT_VIS] Video frame rate |
+
+**Examples:**
+
+```bash
+# 📁 Process directory (defaults to png/jpg/jpeg)
+da3 images ./image_folder --export-dir ./output
+
+# 🎯 Custom extensions
+da3 images ./dataset --image-extensions "png,jpg,webp" --export-dir ./output
+
+# 🔧 Use backend service
+da3 images ./dataset \
+ --use-backend \
+ --backend-url http://localhost:8008 \
+ --export-dir ./output
+```
+
+---
+
+### 🎬 video - Video Processing
+
+Process video by extracting frames for depth estimation.
+
+**Usage:**
+
+```bash
+da3 video VIDEO_PATH [OPTIONS]
+```
+
+**Parameters:**
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `VIDEO_PATH` | str | Required | Input video file path |
+| `--fps` | float | `1.0` | Frame extraction sampling FPS |
+| `--model-dir` | str | Default model | Model directory path |
+| `--export-dir` | str | `debug` | Export directory |
+| `--export-format` | str | `glb` | Export format |
+| `--device` | str | `cuda` | Device to use |
+| `--use-backend` | bool | `False` | Use backend service for inference |
+| `--backend-url` | str | `http://localhost:8008` | Backend service URL |
+| `--process-res` | int | `504` | Processing resolution |
+| `--process-res-method` | str | `upper_bound_resize` | Processing resolution method |
+| `--export-feat` | str | `""` | Export feature layer indices |
+| `--auto-cleanup` | bool | `False` | Automatically clean export directory |
+| `--use-ray-pose` | bool | `False` | Use ray-based pose estimation instead of camera decoder |
+| `--ref-view-strategy` | str | `saddle_balanced` | Reference view selection strategy. See [docs](funcs/ref_view_strategy.md) |
+| `--conf-thresh-percentile` | float | `40.0` | [GLB] Confidence threshold percentile |
+| `--num-max-points` | int | `1000000` | [GLB] Maximum number of points |
+| `--show-cameras` | bool | `True` | [GLB] Show cameras |
+| `--feat-vis-fps` | int | `15` | [FEAT_VIS] Video frame rate |
+
+**Examples:**
+
+```bash
+# ✨ Basic video processing
+da3 video path/to/video.mp4 --export-dir ./output
+
+# ⚙️ Control frame sampling and resolution
+da3 video path/to/video.mp4 \
+ --fps 2.0 \
+ --process-res 1024 \
+ --export-dir ./output
+
+# 🔧 Use backend service
+da3 video path/to/video.mp4 \
+ --use-backend \
+ --backend-url http://localhost:8008 \
+ --export-dir ./output
+```
+
+---
+
+### 📐 colmap - COLMAP Dataset Processing
+
+Run pose-conditioned depth estimation on COLMAP data.
+
+**Usage:**
+
+```bash
+da3 colmap COLMAP_DIR [OPTIONS]
+```
+
+**Parameters:**
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `COLMAP_DIR` | str | Required | COLMAP directory containing `images/` and `sparse/` subdirectories |
+| `--sparse-subdir` | str | `""` | Sparse reconstruction subdirectory (e.g., `"0"` for `sparse/0/`) |
+| `--align-to-input-ext-scale` | bool | `True` | Align prediction to input extrinsics scale |
+| `--model-dir` | str | Default model | Model directory path |
+| `--export-dir` | str | `debug` | Export directory |
+| `--export-format` | str | `glb` | Export format |
+| `--device` | str | `cuda` | Device to use |
+| `--use-backend` | bool | `False` | Use backend service for inference |
+| `--backend-url` | str | `http://localhost:8008` | Backend service URL |
+| `--process-res` | int | `504` | Processing resolution |
+| `--process-res-method` | str | `upper_bound_resize` | Processing resolution method |
+| `--export-feat` | str | `""` | Export feature layer indices |
+| `--auto-cleanup` | bool | `False` | Automatically clean export directory |
+| `--use-ray-pose` | bool | `False` | Use ray-based pose estimation instead of camera decoder |
+| `--ref-view-strategy` | str | `saddle_balanced` | Reference view selection strategy. See [docs](funcs/ref_view_strategy.md) |
+| `--conf-thresh-percentile` | float | `40.0` | [GLB] Confidence threshold percentile |
+| `--num-max-points` | int | `1000000` | [GLB] Maximum number of points |
+| `--show-cameras` | bool | `True` | [GLB] Show cameras |
+| `--feat-vis-fps` | int | `15` | [FEAT_VIS] Video frame rate |
+
+**Examples:**
+
+```bash
+# 📐 Process COLMAP dataset
+da3 colmap ./colmap_dataset --export-dir ./output
+
+# 🎯 Use specific sparse subdirectory and align scale
+da3 colmap ./colmap_dataset \
+ --sparse-subdir 0 \
+ --align-to-input-ext-scale \
+ --export-dir ./output
+
+# 🔧 Use backend service
+da3 colmap ./colmap_dataset \
+ --use-backend \
+ --backend-url http://localhost:8008 \
+ --export-dir ./output
+```
+
+---
+
+### 🔧 backend - Backend Service
+
+Start model backend service with integrated gallery.
+
+**Usage:**
+
+```bash
+da3 backend [OPTIONS]
+```
+
+**Parameters:**
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `--model-dir` | str | Default model | Model directory path |
+| `--device` | str | `cuda` | Device to use |
+| `--host` | str | `127.0.0.1` | Host address to bind to |
+| `--port` | int | `8008` | Port number to bind to |
+| `--gallery-dir` | str | Default gallery dir | Gallery directory path (optional) |
+
+**Features:**
+- 🎯 Keeps model resident in GPU memory
+- 🔌 Provides REST inference API
+- 📊 Integrated dashboard and status monitoring
+- 🖼️ Optional gallery browser (if `--gallery-dir` is provided)
+
+**Available Endpoints:**
+- 🏠 `/` - Home page
+- 📊 `/dashboard` - Dashboard
+- ✅ `/status` - API status
+- 🖼️ `/gallery/` - Gallery browser (if enabled)
+
+**Examples:**
+
+```bash
+# 🚀 Basic backend service
+da3 backend --model-dir depth-anything/DA3NESTED-GIANT-LARGE
+
+# 🖼️ Backend with gallery
+da3 backend \
+ --model-dir depth-anything/DA3NESTED-GIANT-LARGE \
+ --device cuda \
+ --host 0.0.0.0 \
+ --port 8008 \
+ --gallery-dir ./workspace
+
+# 💻 Use CPU
+da3 backend --model-dir depth-anything/DA3NESTED-GIANT-LARGE --device cpu
+```
+
+---
+
+### 🎨 gradio - Gradio Application
+
+Launch Depth Anything 3 Gradio interactive web application.
+
+**Usage:**
+
+```bash
+da3 gradio [OPTIONS]
+```
+
+**Parameters:**
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `--model-dir` | str | Required | Model directory path |
+| `--workspace-dir` | str | Required | Workspace directory path |
+| `--gallery-dir` | str | Required | Gallery directory path |
+| `--host` | str | `127.0.0.1` | Host address to bind to |
+| `--port` | int | `7860` | Port number to bind to |
+| `--share` | bool | `False` | Create a public link |
+| `--debug` | bool | `False` | Enable debug mode |
+| `--cache-examples` | bool | `False` | Pre-cache all example scenes at startup |
+| `--cache-gs-tag` | str | `""` | Tag to match scene names for high-res+3DGS caching |
+
+**Examples:**
+
+```bash
+# 🎨 Basic Gradio application
+da3 gradio \
+ --model-dir depth-anything/DA3NESTED-GIANT-LARGE \
+ --workspace-dir ./workspace \
+ --gallery-dir ./gallery
+
+# 🌐 Enable sharing and debug
+da3 gradio \
+ --model-dir depth-anything/DA3NESTED-GIANT-LARGE \
+ --workspace-dir ./workspace \
+ --gallery-dir ./gallery \
+ --share \
+ --debug
+
+# ⚡ Pre-cache examples
+da3 gradio \
+ --model-dir depth-anything/DA3NESTED-GIANT-LARGE \
+ --workspace-dir ./workspace \
+ --gallery-dir ./gallery \
+ --cache-examples \
+ --cache-gs-tag "dl3dv"
+```
+
+---
+
+### 🖼️ gallery - Gallery Server
+
+Launch standalone Depth Anything 3 Gallery server.
+
+**Usage:**
+
+```bash
+da3 gallery [OPTIONS]
+```
+
+**Parameters:**
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `--gallery-dir` | str | Default gallery dir | Gallery root directory |
+| `--host` | str | `127.0.0.1` | Host address to bind to |
+| `--port` | int | `8007` | Port number to bind to |
+| `--open-browser` | bool | `False` | Open browser after launch |
+
+**Note:**
+The gallery expects each scene folder to contain at least `scene.glb` and `scene.jpg`, with optional subfolders such as `depth_vis/` or `gs_video/`.
+
+**Examples:**
+
+```bash
+# 🖼️ Basic gallery server
+da3 gallery --gallery-dir ./workspace
+
+# 🌐 Custom host and port
+da3 gallery \
+ --gallery-dir ./workspace \
+ --host 0.0.0.0 \
+ --port 8007
+
+# 🚀 Auto-open browser
+da3 gallery --gallery-dir ./workspace --open-browser
+```
+
+---
+
+## ⚙️ Parameter Details
+
+### 🔧 Common Parameters
+
+- **`--export-dir`**: Output directory, defaults to `debug`
+- **`--export-format`**: Export format, supports combining multiple formats with hyphens:
+ - 📦 `mini_npz`: Compressed NumPy format
+ - 🎨 `glb`: glTF binary format (3D scene)
+ - 🔍 `feat_vis`: Feature visualization
+ - Example: `mini_npz-glb` exports both formats
+
+- **`--process-res`** / **`--process-res-method`**: Control preprocessing resolution strategy
+ - `process-res`: Target resolution (default 504)
+ - `process-res-method`: Resize method (default `upper_bound_resize`)
+
+- **`--auto-cleanup`**: Remove existing export directory without confirmation
+
+- **`--use-backend`** / **`--backend-url`**: Reuse running backend service
+ - ⚡ Reduces model loading time
+ - 🌐 Supports distributed processing
+
+- **`--export-feat`**: Layer indices for exporting intermediate features (comma-separated)
+ - Example: `"9,19,29,39"`
+
+### 🎨 GLB Export Parameters
+
+- **`--conf-thresh-percentile`**: Lower percentile for adaptive confidence threshold (default 40.0)
+ - Used to filter low-confidence points
+
+- **`--num-max-points`**: Maximum number of points in point cloud (default 1,000,000)
+ - Controls output file size and performance
+
+- **`--show-cameras`**: Show camera wireframes in exported scene (default True)
+
+### 🔍 Feature Visualization Parameters
+
+- **`--feat-vis-fps`**: Frame rate for feature visualization output video (default 15)
+
+### 🎬 Video-Specific Parameters
+
+- **`--fps`**: Video frame extraction sampling rate (default 1.0 FPS)
+ - Higher values extract more frames
+
+### 📐 COLMAP-Specific Parameters
+
+- **`--sparse-subdir`**: Sparse reconstruction subdirectory
+ - Empty string uses `sparse/` directory
+ - `"0"` uses `sparse/0/` directory
+
+- **`--align-to-input-ext-scale`**: Align prediction to input extrinsics scale (default True)
+ - Ensures depth estimation is consistent with COLMAP scale
+
+---
+
+## 💡 Usage Examples
+
+### 1️⃣ Basic Workflow
+
+```bash
+# 🔧 Start backend service
+da3 backend --model-dir depth-anything/DA3NESTED-GIANT-LARGE --host 0.0.0.0 --port 8008
+
+# 🖼️ Process single image
+da3 image image.jpg --export-dir ./output1 --use-backend
+
+# 🎬 Process video
+da3 video video.mp4 --fps 2.0 --export-dir ./output2 --use-backend
+
+# 📐 Process COLMAP dataset
+da3 colmap ./colmap_data --export-dir ./output3 --use-backend
+```
+
+### 2️⃣ Using Auto Mode
+
+```bash
+# 🤖 Auto-detect and process
+da3 auto ./unknown_input --export-dir ./output
+
+# ⚡ With backend acceleration
+da3 auto ./unknown_input \
+ --use-backend \
+ --backend-url http://localhost:8008 \
+ --export-dir ./output
+```
+
+### 3️⃣ Multi-Format Export
+
+```bash
+# 📦 Export both NPZ and GLB formats
+da3 auto assets/examples/SOH \
+ --export-format mini_npz-glb \
+ --export-dir ./workspace/soh
+
+# 🔍 Export feature visualization
+da3 image image.jpg \
+ --export-format feat_vis \
+ --export-feat "9,19,29,39" \
+ --export-dir ./results
+```
+
+### 4️⃣ Advanced Configuration
+
+```bash
+# ⚙️ Custom resolution and point cloud density
+da3 image image.jpg \
+ --process-res 1024 \
+ --num-max-points 2000000 \
+ --conf-thresh-percentile 30.0 \
+ --export-dir ./output
+
+# 📐 COLMAP advanced options
+da3 colmap ./colmap_data \
+ --sparse-subdir 0 \
+ --align-to-input-ext-scale \
+ --process-res 756 \
+ --export-dir ./output
+```
+
+### 5️⃣ Batch Processing Workflow
+
+```bash
+# 🔧 Start backend
+da3 backend \
+ --model-dir depth-anything/DA3NESTED-GIANT-LARGE \
+ --device cuda \
+ --host 0.0.0.0 \
+ --port 8008 \
+ --gallery-dir ./workspace
+
+# 🔄 Batch process multiple scenes
+for scene in scene1 scene2 scene3; do
+ da3 auto ./data/$scene \
+ --export-dir ./workspace/$scene \
+ --use-backend \
+ --auto-cleanup
+done
+
+# 🖼️ Launch gallery to view results
+da3 gallery --gallery-dir ./workspace --open-browser
+```
+
+### 6️⃣ Web Applications
+
+```bash
+# 🎨 Launch Gradio application
+da3 gradio \
+ --model-dir depth-anything/DA3NESTED-GIANT-LARGE \
+ --workspace-dir workspace/gradio \
+ --gallery-dir ./gallery \
+ --host 0.0.0.0 \
+ --port 7860 \
+ --share
+```
+
+### 7️⃣ Transformer Feature Visualization
+
+```bash
+# 🔍 Export Transformer features
+# 📦 Combined with numerical output
+da3 auto video.mp4 \
+ --export-format glb-feat_vis \
+ --export-feat "11,21,31" \
+ --export-dir ./debug \
+ --use-backend
+```
+
+---
+
+## 📝 Notes
+
+1. **🔧 Backend Service**: Recommended for processing multiple tasks to improve efficiency
+2. **💾 GPU Memory**: Be mindful of GPU memory usage when processing high-resolution inputs
+3. **📁 Export Directory**: Use `--auto-cleanup` to avoid manual confirmation for deletion
+4. **🔀 Format Combination**: Multiple export formats can be combined with hyphens (e.g., `mini_npz-glb-feat_vis`)
+5. **📐 COLMAP Data**: Ensure COLMAP directory structure is correct (contains `images/` and `sparse/` subdirectories)
+
+---
+
+## ❓ Getting Help
+
+View detailed help for any command:
+
+```bash
+# 📖 View main help
+da3 --help
+
+# 🔍 View specific command help
+da3 auto --help
+da3 image --help
+da3 backend --help
+```
diff --git a/docs/funcs/ref_view_strategy.md b/docs/funcs/ref_view_strategy.md
new file mode 100644
index 0000000000000000000000000000000000000000..840f27293653557e323d4b3aae29fbc88ec738de
--- /dev/null
+++ b/docs/funcs/ref_view_strategy.md
@@ -0,0 +1,183 @@
+# 📐 Reference View Selection Strategy
+
+## 📖 Overview
+
+Reference view selection is a component in multi-view depth estimation. When processing multiple input views, the model needs to determine which view should serve as the primary reference frame for depth prediction, defining the world coordinate system.
+
+Different reference view will leads to different reconstruction results. This is a known consideration in multi-view geometry and was analyzed in [PI3](https://arxiv.org/abs/2507.13347). The choice of reference view can affect the quality and consistency of depth predictions across the scene.
+
+
+## 🚀 Our Simple Solution: Automatic Reference View Selection
+
+DA3 provides a simple approach to address this through **automatic reference view selection** based on **class tokens**. Instead of relying on heuristics or manual selection, the model analyzes the class token features from all input views and intelligently selects the most suitable reference frame.
+
+---
+
+## 🎨 Available Strategies
+
+### 1. ⚖️ `saddle_balanced` (Recommended, Default)
+
+**Philosophy:**
+Select a view that achieves balance across multiple feature metrics. This strategy looks for a "middle ground" view that is neither too similar nor too different from other views, making it a stable reference point.
+
+**How it works:**
+1. Extracts and normalizes class tokens from all views
+2. Computes three complementary metrics for each view:
+ - **Similarity score**: Average cosine similarity with other views
+ - **Feature norm**: L2 norm of the original features
+ - **Feature variance**: Variance across feature dimensions
+3. Normalizes each metric to [0, 1] range
+4. Selects the view closest to 0.5 (median) across all three metrics
+
+### 2. 🎢 `saddle_sim_range`
+
+**Philosophy:**
+Select a view with the largest similarity range to other views. This identifies "saddle point" views that are highly similar to some views but dissimilar to others, making them information-rich anchor points.
+
+**How it works:**
+1. Computes pairwise cosine similarity between all views
+2. For each view, calculates the range (max - min) of similarities to other views
+3. Selects the view with the maximum similarity range
+
+---
+
+### 3. 1️⃣ `first` (Not Recommended)
+
+**Philosophy:**
+Always use the first view in the input sequence as the reference.
+
+**How it works:**
+Simply returns index 0.
+
+**When to use:**
+- ⛔ **Not recommended** in general
+- 🔧 Only use when you have manually pre-sorted your views and know the first view is optimal
+- 🐛 Debugging or baseline comparisons
+
+---
+
+### 4. ⏸️ `middle`
+
+**Philosophy:**
+Select the view in the middle of the input sequence.
+
+**How it works:**
+Returns the view at index `S // 2` where S is the number of views.
+
+**When to use:**
+- ⏱️ **Only recommended when input images are temporally ordered**
+- 🎬 Video sequences (e.g., **DA3-LONG** setting)
+- 📹 Sequential captures where the middle frame likely has the most stable viewpoint
+
+**Specific use case: DA3-LONG** 🎬
+In video-based depth estimation scenarios (like DA3-LONG), where inputs are consecutive frames, `middle` is often the **optimal choice** because that it has maximum overlap with all other frames.
+
+
+## 💻 Usage
+
+### 🐍 Python API
+
+```python
+from depth_anything_3 import DepthAnything3
+
+model = DepthAnything3.from_pretrained("depth-anything/DA3NESTED-GIANT-LARGE")
+
+# Use default (saddle_balanced)
+prediction = model.inference(
+ images,
+ ref_view_strategy="saddle_balanced"
+)
+
+# For video sequences, consider using middle
+prediction = model.inference(
+ video_frames,
+ ref_view_strategy="middle" # Good for temporal sequences
+)
+
+# For complex scenes with wide baselines
+prediction = model.inference(
+ images,
+ ref_view_strategy="saddle_sim_range"
+)
+```
+
+### 🖥️ Command Line Interface
+
+```bash
+# Default (saddle_balanced)
+da3 auto input/ --export-dir output/
+
+# Explicitly specify strategy
+da3 auto input/ --ref-view-strategy saddle_balanced
+
+# For video processing
+da3 video input.mp4 --ref-view-strategy middle
+
+# For wide-baseline multi-view
+da3 images captures/ --ref-view-strategy saddle_sim_range
+```
+
+---
+
+### 🎯 When Selection Is Applied
+
+Reference view selection is applied when:
+- 3️⃣ Number of views S ≥ 3
+
+---
+
+## 💡 Recommendations
+
+### 📋 Quick Guide
+
+| Scenario | Recommended Strategy | Rationale |
+|----------|---------------------|-----------|
+| **Default / Unknown** | `saddle_balanced` | Robust, balanced, works well across diverse scenarios |
+| **Video frames** | `middle` | Temporal coherence, stable middle frame |
+| **Wide-baseline multi-view** | `saddle_sim_range` | Maximizes information coverage |
+| **Pre-sorted inputs** | `first` | Use only if you've manually optimized ordering |
+| **Single image** | `first` | Automatically used (no reordering needed for S ≤ 2) |
+
+### ✨ Best Practices
+
+1. 🎯 **Start with defaults**: `saddle_balanced` works well in most cases
+2. 🎬 **Consider your input type**: Use `middle` for videos, `saddle_balanced` for photos
+3. 🔬 **Experiment if needed**: Try different strategies if results are suboptimal
+4. 📊 **Monitor performance**: Check `glb` quality and consistency across views.
+
+---
+
+## 🔧 Technical Details
+
+### 🎚️ Selection Threshold
+
+The reference view selection is only triggered when:
+```python
+num_views >= 3 # At least 3 views required
+```
+
+For 1-2 views, no reordering is performed (equivalent to using `first`).
+
+### ⚙️ Implementation
+
+The selection happens at layer `alt_start - 1` in the vision transformer, before the first global attention layer. This ensures the selected reference view influences the entire depth prediction pipeline.
+
+---
+
+## ❓ FAQ
+
+**Q: 🤔 Why is this feature provided?**
+A: The model can handle any view order, but this feature provides automatic optimization for reference view selection, which can help improve depth prediction quality in multi-view scenarios.
+
+**Q: ⏱️ Does this add computational cost?**
+A: The overhead is totally negligible.
+
+**Q: 🎮 Can I manually specify which view to use as reference?**
+A: Not directly through this parameter. You can pre-sort your input images to place your preferred reference view first and use `ref_view_strategy="first"`.
+
+**Q: ⚙️ What happens if I don't specify this parameter?**
+A: The default `saddle_balanced` strategy is used automatically.
+
+**Q: 📊 Is this feature used in the DA3 paper benchmarks?**
+A: No, the paper used `first` as the default strategy for all multi-view experiments. The current default has been updated to `saddle_balanced` for better robustness.
+
diff --git a/notebooks/da3.ipynb b/notebooks/da3.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9f98cdbb641292f658139d34d3286b71a3b2009c
--- /dev/null
+++ b/notebooks/da3.ipynb
@@ -0,0 +1,150 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Depth Anything 3 (DA3) Usage Example\n",
+ "\n",
+ "This notebook demonstrates how to use Depth Anything 3 for camera poses and depth estimation.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install required packages\n",
+ "# !pip install depth-anything-3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading weights from local directory\n",
+ "Model loaded on cuda\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from PIL import Image\n",
+ "import torch\n",
+ "from depth_anything_3.api import DepthAnything3\n",
+ "from depth_anything_3.utils.visualize import visualize_depth\n",
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ "model = DepthAnything3.from_pretrained(\"depth-anything/DA3NESTED-GIANT-LARGE\")\n",
+ "model = model.to(device)\n",
+ "model.eval()\n",
+ "print(f\"Model loaded on {device}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Depth shape: (2, 280, 504)\n",
+ "Extrinsics: (2, 3, 4)\n",
+ "Intrinsics: (2, 3, 3)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load sample images and run inference\n",
+ "image_paths = [\n",
+ " \"assets/examples/SOH/000.png\",\n",
+ " \"assets/examples/SOH/010.png\"\n",
+ "]\n",
+ "\n",
+ "# Run inference\n",
+ "prediction = model.inference(\n",
+ " image=image_paths,\n",
+ " process_res=504,\n",
+ " process_res_method=\"upper_bound_resize\",\n",
+ " export_dir=None,\n",
+ " export_format=\"glb\"\n",
+ ")\n",
+ "print(f\"Depth shape: {prediction.depth.shape}\")\n",
+ "print(f\"Extrinsics: {prediction.extrinsics.shape if prediction.extrinsics is not None else 'None'}\")\n",
+ "print(f\"Intrinsics: {prediction.intrinsics.shape if prediction.intrinsics is not None else 'None'}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Visualize input images and depth maps\n",
+ "n_images = prediction.depth.shape[0]\n",
+ "\n",
+ "fig, axes = plt.subplots(2, n_images, figsize=(12, 6))\n",
+ "\n",
+ "if n_images == 1:\n",
+ " axes = axes.reshape(2, 1)\n",
+ "\n",
+ "for i in range(n_images):\n",
+ " # Show original image\n",
+ " if prediction.processed_images is not None:\n",
+ " axes[0, i].imshow(prediction.processed_images[i])\n",
+ " axes[0, i].set_title(f\"Input {i+1}\")\n",
+ " axes[0, i].axis('off')\n",
+ " \n",
+ " # Show depth map\n",
+ " depth_vis = visualize_depth(prediction.depth[i], cmap=\"Spectral\")\n",
+ " axes[1, i].imshow(depth_vis)\n",
+ " axes[1, i].set_title(f\"Depth {i+1}\")\n",
+ " axes[1, i].axis('off')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/da3_tutorial.ipynb b/notebooks/da3_tutorial.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b1696ac030f5f0aa3660e400b643613b0a7b002e
--- /dev/null
+++ b/notebooks/da3_tutorial.ipynb
@@ -0,0 +1,667 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 🌊 Depth Anything 3 — From Images to 3D in Seconds\n",
+ "\n",
+ "
"
+ ""
+ if _gallery_dir and os.path.exists(_gallery_dir)
+ else ""
+ )
+ + """
+
+
+
+
+
+ """
+ )
+ return HTMLResponse(html_content)
+
+ @_app.get("/dashboard", response_class=HTMLResponse)
+ async def dashboard():
+ """HTML dashboard for monitoring backend status and tasks."""
+ if _backend is None:
+ return HTMLResponse("
Backend not initialized
", status_code=500)
+
+ # Get backend status
+ status = _backend.get_status()
+
+ # Safely format status values
+ if status["load_time"] is not None:
+ load_time_str = f"{status['load_time']:.2f}s"
+ else:
+ load_time_str = "Not loaded"
+
+ if status["uptime"] is not None:
+ uptime_str = f"{status['uptime']:.2f}s"
+ else:
+ uptime_str = "Not running"
+
+ # Get tasks information
+ active_tasks = [task for task in _tasks.values() if task.status in ["pending", "running"]]
+ completed_tasks = [
+ task for task in _tasks.values() if task.status in ["completed", "failed"]
+ ]
+
+ # Generate task HTML
+ active_tasks_html = ""
+ if active_tasks:
+ for task in active_tasks:
+ task_details = f"""
+
+
+ {task.task_id}
+ {task.status}
+
+
{task.message}
+
+
+ Images: {task.num_images or 'N/A'} |
+ Format: {task.export_format or 'N/A'} |
+ Method: {task.process_res_method or 'N/A'} |
+ Export Dir: {task.export_dir or 'N/A'}
+
+ {f' Video: {task.video_path}' if task.video_path else ''}
+
Last updated: {time.strftime('%Y-%m-%d %H:%M:%S')}
+
+ {active_tasks_html}
+
+
+
+
Recent Completed Tasks
+ {completed_tasks_html}
+
+
+
+
+
+
+ """
+
+ return HTMLResponse(html_content)
+
+ @_app.get("/status")
+ async def get_status():
+ """Get backend status with GPU memory information."""
+ if _backend is None:
+ raise HTTPException(status_code=500, detail="Backend not initialized")
+
+ status = _backend.get_status()
+
+ # Add GPU memory information
+ gpu_memory = get_gpu_memory_info()
+ if gpu_memory:
+ status["gpu_memory"] = {
+ "total_gb": round(gpu_memory["total_gb"], 2),
+ "allocated_gb": round(gpu_memory["allocated_gb"], 2),
+ "reserved_gb": round(gpu_memory["reserved_gb"], 2),
+ "free_gb": round(gpu_memory["free_gb"], 2),
+ "utilization_percent": round(gpu_memory["utilization"], 1),
+ }
+ else:
+ status["gpu_memory"] = None
+
+ return status
+
+ @_app.post("/inference", response_model=InferenceResponse)
+ async def run_inference(request: InferenceRequest):
+ """Submit inference task and return task ID."""
+ global _running_task_id
+
+ if _backend is None:
+ raise HTTPException(status_code=500, detail="Backend not initialized")
+
+ # Generate unique task ID
+ task_id = str(uuid.uuid4())
+
+ # Create task status
+ if _running_task_id is not None:
+ status_msg = f"[{task_id}] Task queued (waiting for {_running_task_id} to complete)"
+ else:
+ status_msg = f"[{task_id}] Task submitted"
+
+ _tasks[task_id] = TaskStatus(
+ task_id=task_id,
+ status="pending",
+ message=status_msg,
+ created_at=time.time(),
+ export_dir=request.export_dir,
+ request=request,
+ # Record essential parameters
+ num_images=len(request.image_paths),
+ export_format=request.export_format,
+ process_res_method=request.process_res_method,
+ video_path=(
+ request.image_paths[0] if request.image_paths else None
+ ), # Use first image path as video reference
+ )
+
+ # Add task to queue
+ _task_queue.append(task_id)
+
+ # If no task is running, start processing the queue
+ if _running_task_id is None:
+ _process_next_task()
+
+ return InferenceResponse(
+ success=True,
+ message="Task submitted successfully",
+ task_id=task_id,
+ export_dir=request.export_dir,
+ export_format=request.export_format,
+ )
+
+ @_app.get("/task/{task_id}", response_model=TaskStatus)
+ async def get_task_status(task_id: str):
+ """Get task status by task ID."""
+ if task_id not in _tasks:
+ raise HTTPException(status_code=404, detail="Task not found")
+
+ return _tasks[task_id]
+
+ @_app.get("/gpu-memory")
+ async def get_gpu_memory():
+ """Get detailed GPU memory information."""
+ gpu_memory = get_gpu_memory_info()
+ if gpu_memory is None:
+ return {
+ "available": False,
+ "message": "CUDA not available or memory info cannot be retrieved",
+ }
+
+ return {
+ "available": True,
+ "total_gb": round(gpu_memory["total_gb"], 2),
+ "allocated_gb": round(gpu_memory["allocated_gb"], 2),
+ "reserved_gb": round(gpu_memory["reserved_gb"], 2),
+ "free_gb": round(gpu_memory["free_gb"], 2),
+ "utilization_percent": round(gpu_memory["utilization"], 1),
+ "status": (
+ "healthy"
+ if gpu_memory["utilization"] < 80
+ else "warning" if gpu_memory["utilization"] < 95 else "critical"
+ ),
+ }
+
+ @_app.get("/tasks")
+ async def list_tasks():
+ """List all tasks."""
+ # Separate active and completed tasks
+ active_tasks = [task for task in _tasks.values() if task.status in ["pending", "running"]]
+ completed_tasks = [
+ task for task in _tasks.values() if task.status in ["completed", "failed"]
+ ]
+
+ return {
+ "tasks": list(_tasks.values()),
+ "active_tasks": active_tasks,
+ "completed_tasks": completed_tasks,
+ "active_count": len(active_tasks),
+ "total_count": len(_tasks),
+ }
+
+ @_app.post("/cleanup")
+ async def manual_cleanup():
+ """Manually trigger task cleanup."""
+ try:
+ _cleanup_old_tasks()
+ return {"message": "Cleanup completed", "active_tasks": len(_tasks)}
+ except Exception as e:
+ raise HTTPException(status_code=500, detail=f"Cleanup failed: {str(e)}")
+
+ @_app.delete("/task/{task_id}")
+ async def delete_task(task_id: str):
+ """Delete a specific task."""
+ if task_id not in _tasks:
+ raise HTTPException(status_code=404, detail="Task not found")
+
+ # Only allow deletion of completed/failed tasks
+ if _tasks[task_id].status not in ["completed", "failed"]:
+ raise HTTPException(status_code=400, detail="Cannot delete running or pending tasks")
+
+ del _tasks[task_id]
+ return {"message": f"Task {task_id} deleted successfully"}
+
+ @_app.post("/reload")
+ async def reload_model():
+ """Reload the model."""
+ if _backend is None:
+ raise HTTPException(status_code=500, detail="Backend not initialized")
+
+ try:
+ _backend.model = None
+ _backend.model_loaded = False
+ _backend.load_model()
+ return {"message": "Model reloaded successfully"}
+ except Exception as e:
+ raise HTTPException(status_code=500, detail=f"Failed to reload model: {str(e)}")
+
+ # ============================================================================
+ # Gallery routes
+ # ============================================================================
+
+ if _gallery_dir and os.path.exists(_gallery_dir):
+ # Load gallery HTML page (with modified paths for /gallery/ subdirectory)
+ _gallery_html = _load_gallery_html()
+
+ @_app.get("/gallery/", response_class=HTMLResponse)
+ @_app.get("/gallery", response_class=HTMLResponse)
+ async def gallery_home():
+ """Gallery home page."""
+ return HTMLResponse(_gallery_html)
+
+ @_app.get("/gallery/manifest.json")
+ async def gallery_manifest():
+ """Get gallery group list."""
+ try:
+ return build_group_list(_gallery_dir)
+ except Exception as e:
+ raise HTTPException(
+ status_code=500, detail=f"Failed to build group list: {str(e)}"
+ )
+
+ @_app.get("/gallery/manifest/{group}.json")
+ async def gallery_group_manifest(group: str):
+ """Get manifest for a specific group."""
+ if not _is_plain_name(group):
+ raise HTTPException(status_code=400, detail="Invalid group name")
+ try:
+ return build_group_manifest(_gallery_dir, group)
+ except Exception as e:
+ raise HTTPException(
+ status_code=500, detail=f"Failed to build group manifest: {str(e)}"
+ )
+
+ @_app.get("/gallery/{path:path}")
+ async def gallery_files(path: str):
+ """Serve gallery static files (GLB, JPG, etc.)."""
+ # Security check: prevent directory traversal
+ path_parts = path.split("/")
+ if any(not _is_plain_name(part) for part in path_parts if part):
+ raise HTTPException(status_code=400, detail="Invalid path")
+
+ file_path = os.path.join(_gallery_dir, *path_parts)
+
+ # Ensure the file is within gallery directory
+ real_file_path = os.path.realpath(file_path)
+ real_gallery_dir = os.path.realpath(_gallery_dir)
+ if not real_file_path.startswith(real_gallery_dir):
+ raise HTTPException(status_code=403, detail="Access denied")
+
+ if not os.path.exists(file_path) or not os.path.isfile(file_path):
+ raise HTTPException(status_code=404, detail="File not found")
+
+ return FileResponse(file_path)
+
+ return _app
+
+
+def start_server(
+ model_dir: str,
+ device: str = "cuda",
+ host: str = "127.0.0.1",
+ port: int = 8000,
+ gallery_dir: Optional[str] = None,
+):
+ """Start the backend server."""
+ app = create_app(model_dir, device, gallery_dir)
+
+ print("Starting Depth Anything 3 Backend...")
+ print(f"Model directory: {model_dir}")
+ print(f"Device: {device}")
+ print(f"Server: http://{host}:{port}")
+ print(f"Dashboard: http://{host}:{port}/dashboard")
+ print(f"API Status: http://{host}:{port}/status")
+
+ if gallery_dir and os.path.exists(gallery_dir):
+ print(f"Gallery: http://{host}:{port}/gallery/")
+
+ print("=" * 60)
+ print("Backend is running! You can now:")
+ print(f" • Open home page: http://{host}:{port}")
+ print(f" • Open dashboard: http://{host}:{port}/dashboard")
+ print(f" • Check API status: http://{host}:{port}/status")
+
+ if gallery_dir and os.path.exists(gallery_dir):
+ print(f" • Browse gallery: http://{host}:{port}/gallery/")
+
+ print(" • Submit inference tasks via API")
+ print("=" * 60)
+
+ uvicorn.run(app, host=host, port=port, log_level="info")
+
+
+if __name__ == "__main__":
+ import argparse
+
+ parser = argparse.ArgumentParser(description="Depth Anything 3 Backend Server")
+ parser.add_argument("--model-dir", required=True, help="Model directory path")
+ parser.add_argument("--device", default="cuda", help="Device to use")
+ parser.add_argument("--host", default="127.0.0.1", help="Host to bind to")
+ parser.add_argument("--port", type=int, default=8000, help="Port to bind to")
+ parser.add_argument("--gallery-dir", help="Gallery directory path (optional)")
+
+ args = parser.parse_args()
+ start_server(args.model_dir, args.device, args.host, args.port, args.gallery_dir)
diff --git a/src/depth_anything_3/services/gallery.py b/src/depth_anything_3/services/gallery.py
new file mode 100644
index 0000000000000000000000000000000000000000..f72bb5e5f6defbc24cf2278a53b7162a8ad5519d
--- /dev/null
+++ b/src/depth_anything_3/services/gallery.py
@@ -0,0 +1,806 @@
+#!/usr/bin/env python3
+# flake8: noqa: E501
+# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Depth Anything 3 Gallery Server (two-level, single-file)
+Now supports paginated depth preview (4 per page).
+"""
+
+import argparse
+import json
+import mimetypes
+import os
+import posixpath
+import sys
+from functools import partial
+from http import HTTPStatus
+from http.server import SimpleHTTPRequestHandler, ThreadingHTTPServer
+from urllib.parse import quote, unquote
+
+# ------------------------------ Embedded HTML ------------------------------ #
+
+HTML_PAGE = r"""
+
+
+
+ Depth Anything 3 Gallery
+
+
+
+
+
+
+
+
+
+
+
Depth Anything 3 Gallery
+
+
+
+
Level 1 shows groups only; click a group to browse scenes and previews.
+
+
+
+
+
+
+ 🎯 Depth Anything 3 Gallery
+
+
+ Explore 3D reconstructions and depth visualizations from Depth Anything 3.
+ Browse through groups of scenes, preview 3D models, and examine depth maps interactively.
+
+
+
+
+
+
+