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-denoise

Install 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==1.5.9

Datasets

Download link: https://drive.google.com/drive/folders/1--MvLnP7dsBgBZiu46H0S32Y1eBa_j6P?usp=sharing

Please extract data.zip to data folder.

Denoise

Reproduce Paper Results

# 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 2

Denoise Regular-Sized 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.py

Please 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}
}

immohann / score-denoise