add links & remove post-processing selection

app.py

@@ -53,9 +53,7 @@ DEMO_POINTCLOUDS = {
 }
 
 
-def initialize_model(
-    checkpoint_path="model.ckpt", config_path="configs/pi3detr.yaml"
-):
+def initialize_model(checkpoint_path="model.ckpt", config_path="configs/pi3detr.yaml"):
     """Initialize the model at startup and store it in the global cache."""
     global PI3DETR_MODEL, MODEL_STATUS
     try:
@@ -255,7 +253,7 @@ def make_figure(
                     y=points[:, 1],
                     z=points[:, 2],
                     mode="lines",
-                    line=dict(color=color, width=5),
+                    line=dict(color=color, width=8),  # CHANGED: increased from 5 to 8
                     name=f"{curve_type} #{curve_id} ({score:.2f})",
                     visible=curve.get("visible_state", True),
                     hoverinfo="text",
@@ -399,11 +397,9 @@ def run_model_inference(
     points: np.ndarray,
     max_points: int = 32768,
     sample_mode: str = "fps",
-    num_queries: int = 256,  # NEW
-    snap_and_fit: bool = False,  # NEW
-    iou_filter: bool = False,  # NEW
+    num_queries: int = 256,
 ) -> list:
-    """Run model inference on the given point cloud (extended with num_queries, snap_and_fit, iou_filter)."""
+    """Run model inference on the given point cloud."""
     global PI3DETR_MODEL
     if model is None:
         model = PI3DETR_MODEL
@@ -423,8 +419,6 @@ def run_model_inference(
             data,
             reverse_norm=True,
             thresholds=None,
-            snap_and_fit=snap_and_fit,  # CHANGED
-            iou_filter=iou_filter,  # CHANGED
         )
         result = output[0]
         curves = process_model_predictions(result)
@@ -509,9 +503,7 @@ def run_model_prediction(
     th_circle: float,
     th_arc: float,
     num_queries: int = 256,
-    snap_and_fit: bool = False,
-    iou_filter: bool = False,
-):  # CHANGED: removed reduction
+):
     # NOTE: display points now handled outside; keep signature (called before adding display pts state)
     # (This wrapper kept for backwards compatibility if needed – we adapt below in new unified version)
     return run_model_prediction_unified(  # type: ignore
@@ -527,8 +519,6 @@ def run_model_prediction(
         th_arc,
         "",
         num_queries,
-        snap_and_fit,
-        iou_filter,
     )
 
 
@@ -545,8 +535,6 @@ def run_model_prediction_unified(
     th_arc: float,
     file_name: str = "",
     num_queries: int = 256,
-    snap_and_fit: bool = False,
-    iou_filter: bool = False,
 ):
     """
     Run model inference and apply initial threshold-based coloring.
@@ -570,9 +558,7 @@ def run_model_prediction_unified(
                 model_pts,
                 max_points=model_max_points,
                 sample_mode=sample_mode,
-                num_queries=num_queries,  # NEW
-                snap_and_fit=snap_and_fit,  # NEW
-                iou_filter=iou_filter,  # NEW
+                num_queries=num_queries,
             )
     except Exception:
         pass
@@ -788,9 +774,7 @@ def run_model_with_display(
     th_arc: float,
     file_name: str = "",
     num_queries: int = 256,
-    snap_and_fit: bool = False,
-    iou_filter: bool = False,
-):  # CHANGED: removed reduction
+):
     """
     Run inference (if model_pts present) then immediately apply current display
     (max_points/point_size/show_axes) and thresholds. Returns:
@@ -813,10 +797,8 @@ def run_model_with_display(
         th_circle,
         th_arc,
         file_name,
-        num_queries,  # NEW
-        snap_and_fit,  # NEW
-        iou_filter,  # NEW
-    )  # CHANGED: removed reduction
+        num_queries,
+    )
 
     # Now apply current display settings & thresholds without re-inference
     fig_final, display_pts = apply_pointcloud_display_settings(
@@ -836,9 +818,43 @@ def run_model_with_display(
 
 with gr.Blocks(title="PI3DETR") as demo:
     gr.Markdown(
-        "# 🥧 PI3DETR: Detection of Sharp 3D CAD Edges [CPU-PREVIEW]\n"
-        "An end-to-end deep learning model for **parametric curve inference** in **3D point clouds** and **meshes**.\n"
-        "Upload a `.xyz`, `.ply`, or `.obj` file to explore curve detection."
+        """
+        # 🥧 PI3DETR: Detection of Sharp 3D CAD Edges [CPU-PREVIEW]
+
+        A novel end-to-end deep learning model for **parametric curve inference** in **3D point clouds** and **meshes**.
+
+        <div style="margin-top: 10px;">
+            <a href="https://arxiv.org/pdf/2509.03262" target="_blank" style="
+                display: inline-block;
+                background-color: #4CAF50;
+                color: white;
+                padding: 8px 16px;
+                text-decoration: none;
+                border-radius: 5px;
+                margin-right: 8px;
+                font-weight: bold;
+            ">📄 Paper</a>
+            <a href="https://fafraob.github.io/pi3detr/" target="_blank" style="
+                display: inline-block;
+                background-color: #2196F3;
+                color: white;
+                padding: 8px 16px;
+                text-decoration: none;
+                border-radius: 5px;
+                margin-right: 8px;
+                font-weight: bold;
+            ">🌐 Website</a>
+            <a href="https://github.com/fafraob/pi3detr" target="_blank" style="
+                display: inline-block;
+                background-color: #333;
+                color: white;
+                padding: 8px 16px;
+                text-decoration: none;
+                border-radius: 5px;
+                font-weight: bold;
+            ">🐙 GitHub</a>
+        </div>
+        """
     )
 
     with gr.Row():
@@ -857,7 +873,7 @@ with gr.Blocks(title="PI3DETR") as demo:
         with gr.Column():
             gr.Markdown(
                 "### 🎯 Confidence Thresholds\n"
-                "- Hover to inspect scores\n."
+                "- Hover to inspect scores.\n"
                 "- Filter curves by **class confidence** interactively"
             )
     with gr.Row():
@@ -866,15 +882,14 @@ with gr.Blocks(title="PI3DETR") as demo:
                 "### 🧠 Model Settings\n"
                 "- **Sampling Mode:** Choose downsampling strategy.\n"
                 "- **Model Input Size:** Number of model input points.\n"
-                "- **Queries:** Transformer decoder queries (max. output curves).\n"
-                "- Optional: *Snap&Fit* / *IOU-Filter* post-processing."
+                "- **Queries:** Transformer decoder queries (max. output curves)."
             )
         with gr.Column():
             gr.Markdown(
                 "### ⚡ Performance Notes\n"
                 "- Trained on **human-made objects**.\n"
                 "- Optimized for **GPU**; this demo runs on **CPU**.\n"
-                "- For full qualitative performance: \n"
+                "- For **full qualitative performance**: \n"
                 "[GitHub → PI3DETR](https://github.com/fafraob/pi3detr)"
             )
         with gr.Column():
@@ -927,16 +942,13 @@ with gr.Blocks(title="PI3DETR") as demo:
                 step=1,
                 label="Number of Queries",
             )
-            with gr.Row():
-                snap_and_fit_chk = gr.Checkbox(value=True, label="Snap&Fit")
-                iou_filter_chk = gr.Checkbox(value=False, label="IOU-Filter")
 
             # Threshold sliders (no auto-change triggers)
             gr.Markdown("#### Confidence Thresholds (per class)")
-            th_bspline = gr.Slider(0.0, 1.0, value=0.7, step=0.01, label="BSpline ≥")
-            th_line = gr.Slider(0.0, 1.0, value=0.7, step=0.01, label="Line ≥")
-            th_circle = gr.Slider(0.0, 1.0, value=0.7, step=0.01, label="Circle ≥")
-            th_arc = gr.Slider(0.0, 1.0, value=0.7, step=0.01, label="Arc ≥")
+            th_bspline = gr.Slider(0.0, 1.0, value=0.9, step=0.01, label="BSpline ≥")
+            th_line = gr.Slider(0.0, 1.0, value=0.9, step=0.01, label="Line ≥")
+            th_circle = gr.Slider(0.0, 1.0, value=0.9, step=0.01, label="Circle ≥")
+            th_arc = gr.Slider(0.0, 1.0, value=0.9, step=0.01, label="Arc ≥")
 
         with gr.Column(scale=1):
             main_plot = gr.Plot(
@@ -973,8 +985,6 @@ with gr.Blocks(title="PI3DETR") as demo:
             th_arc,
             file_name_state,
             num_queries,
-            snap_and_fit_chk,
-            iou_filter_chk,
         ],
         outputs=[main_plot, curves_state, display_pts_state],
     )