Comments (6)
Thanks I will try to make an update sometime next week.
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Hi @ozansener @SimonVandenhende ,
The disparity data read from the Cityscapes dataset needs to be first processed to be converted to depth:
depth = baseline * fx / disparity,
however, I cannot find this step anywhere in the code?
And in this issue (#2 (comment)) it seems the depth is the metric output by the code?
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Hi @ozansener ,
I just found that in the table of the paper you reported "1 / Disparity_Error", so is it Disparity instead of Depth that you are reporting? I found that the numbers in your paper(Disparity Error before using 1 to divide it) very different from those reported by Kendall et al(http://openaccess.thecvf.com/content_cvpr_2018/papers/Kendall_Multi-Task_Learning_Using_CVPR_2018_paper.pdf)
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If I remember correctly, the disparity error is reported, and not the absolute depth.
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We are reporting disparity in the pixel space after normalization. I know it is not a standard metric and has no real practical meaning especially since it is normalized, but all algorithms are reported similarly. You can look at the disparity error in Table 4. Since this is L1 distance, you can convert all of them to the depth L1 error with the formula you posted. You also need to multiply with the DEPTH_STD value in the code to get metric depth error.
The original issue (masking bug) still exists. This bug-fix is unfortunately getting continuously delayed. I have no ETA for it at the moment. But, you can contact me if this bug is impacting you in some-way.
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Thanks for the clarification!
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Related Issues (20)
- depth loss and metric
- Connection between fmp and task branches HOT 4
- IndexError: list index out of range (when reading base_path in "celeba_loader.py" line 52 )
- size of the segmentation output HOT 1
- run error, Please help me, Thanks HOT 1
- Question about the MinNormSolver
- Unable to obtain the same average error as Table 1 in the paper
- Unable to obtain the same average error as Table 1 in the paper HOT 1
- How to convert .pkl file to .onnx model. I mean there are two parts of the whole model. HOT 4
- Redundancy of optimiser.zero_grad()? HOT 2
- cityscape dataset
- About Cityscapes Instance Segmentation GroundTruth Generation.
- `iter_count` is never incremented in `min_norm_solvers`?
- is the code corresponding to the algorithm in paper ? HOT 1
- If I want to train the 3D image data using MTL by MGDA do I have to change the code min_norm_solver.py? HOT 1
- local variable 'sol' referenced before assignment HOT 1
- Algorithm 2 line 10 HOT 1
- Is there any difference between soft sharing and hard sharing network when using MGDA? HOT 1
- A question about the output of rep HOT 1
- params.json HOT 1
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