[Paper] https://arxiv.org/abs/2107.10981
The code has been tested in the following environment:
| Package | Version | Comment |
|---|---|---|
| PyTorch | 1.9.0 | |
| point_cloud_utils | 0.18.0 | For evaluation only. It loads meshes to compute point-to-mesh distances. |
| pytorch3d | 0.5.0 | For evaluation only. It computes point-to-mesh distances. |
| pytorch-cluster | 1.5.9 | We only use fps (farthest point sampling) to merge denoised patches. |
conda env create -f env.yml conda activate score-denoiseconda create --name score-denoise python=3.8 conda activate score-denoise conda install pytorch==1.9.0 torchvision==0.10.0 cudatoolkit=11.1 -c pytorch -c nvidia conda install -c conda-forge tqdm scipy scikit-learn pyyaml easydict tensorboard pandas # point_cloud_utils conda install -c conda-forge point_cloud_utils==0.18.0 # Pytorch3d conda install -c fvcore -c iopath -c conda-forge fvcore iopath conda install -c pytorch3d pytorch3d==0.5.0 # pytorch-scatter conda install -c pyg pytorch-cluster==1.5.9Download link: https://drive.google.com/drive/folders/1--MvLnP7dsBgBZiu46H0S32Y1eBa_j6P?usp=sharing
Please extract data.zip to data folder.
[Known Issue about the PCNet Testset] The P2M results of the PCNet testset might vary depending on GPU architecture and PyTorch version. However, no matter how it varies, it remains strong linear correlation to the CD metric (see discussion here), so it does not affect the main result of this work.
# PUNet dataset, 10K Points python test.py --dataset PUNet --resolution 10000_poisson --noise 0.01 --niters 1 python test.py --dataset PUNet --resolution 10000_poisson --noise 0.02 --niters 1 python test.py --dataset PUNet --resolution 10000_poisson --noise 0.03 --niters 2 # PUNet dataset, 50K Points python test.py --dataset PUNet --resolution 50000_poisson --noise 0.01 --niters 1 python test.py --dataset PUNet --resolution 50000_poisson --noise 0.02 --niters 1 python test.py --dataset PUNet --resolution 50000_poisson --noise 0.03 --niters 2python test_single.py --input_xyz <input_xyz_path> --output_xyz <output_xyz_path>You may also barely run python test_single.py to see a quick example.
python test_large.py --input_xyz <input_xyz_path> --output_xyz <output_xyz_path>You may also barely run python test_large.py to see a quick example.
python train.pyPlease find tunable parameters in the script.
@InProceedings{Luo_2021_ICCV, author = {Luo, Shitong and Hu, Wei}, title = {Score-Based Point Cloud Denoising}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {4583-4592} }