Comments (1)
Hi, thanks for pointing that out. It's fixed in the latest commit. This bug have no effect on the benchmark results, because neither the variables are used in the code.
However, this could be the reason why in my early experiments, adding an adversarial loss doesn't help the performance... I was inputting the GT- instead of the predicted- transformed shapes into the discriminator...
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Related Issues (11)
- Pretrained models to reproduce paper's results HOT 4
- May I suggest a more general library for saving point clouds, other than open3d? HOT 4
- Install problem HOT 7
- The 'info file' for splitting train/val list in trivial training HOT 2
- Some clarification questions HOT 22
- Including a new loss in the computation graph HOT 6
- Problem with scripts/vis.py HOT 10
- Pretrained models HOT 23
- Unbroken models HOT 6
- Experimental settings HOT 4
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