def compute_subpixel_step(self) -> Tensor:
        """Compute the inverse depth step for sub pixel accurate sampling of the depth cost volume, per camera.

        Szeliski, Richard, and Daniel Scharstein. "Symmetric sub-pixel stereo matching." European Conference on Computer
        Vision. Springer Berlin Heidelberg, 2002.
        """
        delta_d = 0.01
        xy_m1 = self._compute_projection(self.width / 2, self.height / 2, 1.0 - delta_d)
        xy_p1 = self._compute_projection(self.width / 2, self.height / 2, 1.0 + delta_d)
        dx = torch.norm((xy_p1 - xy_m1), 2, dim=-1) / 2.0
        dxdd = dx / (delta_d)  # pixel*(1/meter)
        # half pixel sampling, we're interested in the min for all cameras
        return torch.min(0.5 / dxdd)
Compute the inverse depth step for sub pixel accurate sampling of the depth cost volume, per camera.

        Szeliski, Richard, and Daniel Scharstein. "Symmetric sub-pixel stereo matching." European Conference on Computer
        Vision. Springer Berlin Heidelberg, 2002.
        
kornia/geometry/depth.py