Comments (3)
@tareeqav If you are interested, these two repos might give you an idea and help you with the hybrid loss:
- semodepth inference is public
- semidepth inference and training are public, and the code is based on monodepth v1.0 code(Goddard et al. you mentioned).
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@a-jahani many thanks! looks very promising
from bts.
Yes, you are correct.
We are currently working on a forked project which uses a hybrid training loss defined in terms of photometric reconstruction error and direct supervision.
With this strategy, we can train our bts using both types of dataset: stereo pairs and images with gt.
Thanks for your interest in our work but sorry for that we have no room to give you ragarding this project.
However, you still have a chance to go on your own project if you have a new idea.
Cheers!
Best wishes,
Jin Han Lee
from bts.
Related Issues (20)
- Questions on the code organization
- Pretrain models
- How were the files selected in nyudepthv2_train_files_with_gt.txt? HOT 1
- Why does the depth output has smears on the left and right sides?
- what is the focal HOT 2
- Single machine with multiple gpus,but I only want one...
- Error in filenames file while training
- Can you please tell me how to figure out the actual depth value(in metres or centimeteres) at any (x,y) coordinate of the image that is being predicted by the model? HOT 2
- nyu test dataset HOT 1
- transform
- 解决运行extract_official_train_test_set_from_mat.py时only integer scalar arrays can be converted to a scalar index问题 HOT 3
- kitti test dataset
- Link download fail
- The script does not download all the data and all dense maps
- Why you use mask = depth_gt > 0.1 ?
- OSError: unrecognized data strear contents when reading image file
- wget nyu_depth_v2_labeled.mat: connection refused HOT 2
- train loss nan in HOT 2
- confused about gt in new nyu2dataset and old nyu2dataset HOT 1
- How to avoid NaN in training
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