Comments (3)
YvanYin
commented on June 18, 2024
Our Image-level regression loss only need to normalize the ground truth depth. By contrast, MiDaS has to processing both the ground truth and predicted depth. The process can be the normalization or least square fitting. According to our ablation studies, our loss can achieve better performance.
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mzy97
commented on June 18, 2024
Ok, do you have any idea why your proposed loss better than MiDaS's? In the paper, it seems just a numerical comparison but not given some explanation.
Thank you
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YvanYin
commented on June 18, 2024
MiDaS loss has to adjust both the ground truth and prediction to a similar numerical range. I am conjunct that such adjustment may be not that stable.
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Related Issues (20)
- loss becomes nan HOT 4
- Camera Position HOT 3
- Some questions about train data HOT 6
- PCM training code? HOT 1
- 360 point cloud animation video HOT 1
- Strange size of output files for train HOT 1
- cannot import name 'sparse_collate_fn' from 'torch_sparse.utils' HOT 1
- #@title Download results HOT 2
- No locally scaled-aligned (lwlr_depth ) code found HOT 1
- KeyError: 'model_state_dict' HOT 4
- How to install torchsparse in Centos? HOT 1
- LeReS in Anaconda on Window - ModuleNotFoundError: No module named 'lib' HOT 2
- own dataset
- Where can I find tools/test_depth.py and tools/test_shape.py? HOT 1
- No module named 'lib' error in AdelaiDepth_ResNet101.ipynb HOT 2
- Where to find "SparseTensor"? HOT 1
- Infinit focal length!!! HOT 1
- Error occurred in LeReS HOT 1
- Error on 'Download' step of Google Collab HOT 3
- how to generate depth data like you provide? HOT 1
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