Comments (5)
We do this for mono because the scale is ambiguous, we use this operation to find a best matched scale to compare with GT, but for stereo, baseline is known, still using this operation will be unfair to compare with other stereo methods.
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Unsupervised Learning of Depth and Ego-Motion from Video
this paper may help you get a better understanding of this setting.
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Thanks for your reply, and I have another question. Have you trained this code with only stereo data? My test results are not so good, but training on MS can bring improvement. I sincerely hope for your help.
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Sorry, I didn't try on the Stereo data. This setting will not train posenet。
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you can reopen it if you have further issues.
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Related Issues (20)
- running infer_singleimage.py in win10 ModuleNotFoundError: No module named 'resource' HOT 1
- it seems that the decoder of the auto-encoder network is not trained in the code HOT 1
- About the training and testing setting. HOT 4
- Is there a way to visually check the output of the FeatureNet HOT 1
- dataset.flag HOT 1
- An error occurred while training my own dataset HOT 2
- How to start non-distributed training HOT 2
- Why use DistOptimizerHook? HOT 1
- Problems using DDP HOT 2
- Train only Image Reconstruction Model HOT 2
- cfg_kitti_fm.py stops while training, expected 4 input channels, but got 3 channels instead
- train on own dataset with fm_joint.cfg HOT 1
- pose question HOT 1
- pose question HOT 2
- feature-metric loss only use the first output of the Autoencoder HOT 2
- Evaluation Issue HOT 1
- how to use multi-gpus training?
- Weights for Monocular-only training
- 关于online refinement的疑问
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