Comments (1)
Details about unsupervised training can be found here:
https://openaccess.thecvf.com/content/ICCV2021/supplemental/Luo_Score-Based_Point_Cloud_ICCV_2021_supplemental.pdf
The code is here:
score-denoise/models/denoise.py
Line 81 in e5fc1fe
from score-denoise.
Related Issues (20)
- When to publish your code? HOT 4
- dataset HOT 4
- Supervised loss function HOT 4
- train results HOT 13
- issue about test dataset
- 定性结果 HOT 1
- About dataset production and baseline HOT 1
- how to make the visual figures which noisy points have yellow color and clean points have blue color? HOT 2
- Is it possible to get the score separately? HOT 5
- Results on a large number of points HOT 3
- AttributeError: module 'distutils' has no attribute 'version' HOT 2
- RuntimeError: Sizes of tensors must match except in dimension 0. Expected size 37712 but got size 31882 for tensor number 1 in the list.
- gaps when used on large-scale point cloud
- Train on larger point clouds
- can not install pytorch3d successfully?
- hello,Can you share the ground truth of PCNET test dataset? HOT 1
- visualization HOT 1
- RuntimeError: Given groups=1, weight of size [128, 63, 1], expected input[100000, 66, 4] to have 63 channels, but got 66 channels instead HOT 1
- PCNet result HOT 1
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from score-denoise.