Hello author,
I have some questions regarding the depth-to-disparity conversion in the depth_splat.py script. The standard formula for converting depth to disparity is typically given as:
$disparity= \frac{depth}{baseline×focal length}$
However, in the implementation, it appears that the camera's internal parameters (such as focal length and baseline) are not explicitly defined.
disp_map = torch.from_numpy(batch_depth).unsqueeze(1).float().cuda()
disp_map = disp_map * 2.0 - 1.0
disp_map = disp_map * max_disp
Could you clarify why these parameters are not explicitly included in the code? Is it because they are implicitly handled elsewhere, or are there assumptions about the camera setup that allow for this simplification?
Additionally, I noticed that the weight map in the depth splitting function is computed as:
weights_map=(1.414) ** weights_map
This operation seems to scale the weight map by a factor of $\sqrt{2}$, but there is no mention of why using this in the paper. Could you explain the rationale behind this specific scaling factor? Is it related to normalization, optimization, or some other aspect of the algorithm?
Hello author,
I have some questions regarding the depth-to-disparity conversion in the depth_splat.py script. The standard formula for converting depth to disparity is typically given as:
However, in the implementation, it appears that the camera's internal parameters (such as focal length and baseline) are not explicitly defined.
Could you clarify why these parameters are not explicitly included in the code? Is it because they are implicitly handled elsewhere, or are there assumptions about the camera setup that allow for this simplification?
Additionally, I noticed that the weight map in the depth splitting function is computed as:
weights_map=(1.414) ** weights_mapThis operation seems to scale the weight map by a factor of$\sqrt{2}$ , but there is no mention of why using this in the paper. Could you explain the rationale behind this specific scaling factor? Is it related to normalization, optimization, or some other aspect of the algorithm?