Comments (3)
Could your tell me what is the 100 means?
It was legacy code and removing the multiplier should not affect the training much. However, the learning rate might require some adjustment.
from self-supervised-depth-completion.
Thank you, by the way, why not add smooth loss in the dense d mode(args.w2 = 0 when mode is d). Have you compared the difference?
from self-supervised-depth-completion.
Thank you, by the way, why not add smooth loss in the dense d mode(args.w2 = 0 when mode is d). Have you compared the difference?
I believe I did the experiment but did not observe improvement. Please let me know if you observe otherwise.
from self-supervised-depth-completion.
Related Issues (20)
- Error while loading "calib_cam_to_cam.txt" - can not reshape the array.
- question about depth-estimation results HOT 2
- What is the network used for single d?
- Why I can't get the result when using the trained model you provided?
- How can I get the result in your paper?
- About extracting trained model HOT 2
- Clip output in model.py
- inference HOT 2
- colorize the depth map HOT 1
- some problem about photometric_loss
- Use your pretrained model: GPU run out of memory. 8.95 gb already allocated
- Save output depth map HOT 1
- dataset extracting
- Training doesn't converge HOT 4
- silog error measurement
- Running Error in train mode sparse+photo HOT 1
- To much warning. HOT 2
- Use Stereo Pair Instead of Temporal Pair for Self-Supervised Training?
- The result cannot be reproduced
- Some questions about the details of the code
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from self-supervised-depth-completion.