Score-Based Point Cloud Denoising (ICCV'21)
[Paper] https://arxiv.org/abs/2107.10981
Installation
Recommended Environment
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. |
Install via Conda (PyTorch 1.9.0 + CUDA 11.1)
conda env create -f env.yml
conda activate score-denoiseInstall Manually
conda 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==1.5.9Datasets
Download link: https://drive.google.com/drive/folders/1--MvLnP7dsBgBZiu46H0S32Y1eBa_j6P?usp=sharing
Please extract data.zip to data folder.
Denoise
Reproduce Paper Results
[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 2Denoise Regular-Size Point Clouds (≤ 50K Points)
python 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.
Denoise Large Point Clouds (> 50K Points)
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.
Train
python train.pyPlease find tunable parameters in the script.
Citation
@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}
}