Score-Based Point Cloud Denoising (ICCV'21)

[Paper] https://arxiv.org/abs/2107.10981

This is re-implementation of Score-Based Point Cloud Denoising for 2022S KAIST 3DML project

Installation

Recommended Environment

The code has been tested in the following environment:

Install via Conda (PyTorch 1.9.0 + CUDA 11.1)

conda env create -f env.yml
conda activate score-denoise

Datasets

Download link: https://drive.google.com/file/d/1ZZ3EON8TTtwoRciT5ThcYU3sTtj9Kj7Z/view?usp=sharing

Please extract score_dataset.zip to data folder. It concludes PU-Net, PCNet, and noisy LiDAR dataset.

Pretrained checkpoints

Download link: https://drive.google.com/file/d/1NsNtRR9qhZRsc4GkUGIpU6FXMZpL5UFB/view?usp=sharing

Please extract checkpoints.zip to checkpoints folder. It concludes basic model(best.pt), ablation study model (ablation2_best.pt), and unsupervised model (unsup_best.pt).

Train

# basic training (supervised)
python train.py

# unsupervised training
python train.py --unsup True

# ablation study 2
python train.py --ablation2 True

Training time takes about 39~40 hours.

Test

# PUNet 10K
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 1 --noise 0.01
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 1 --noise 0.02
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 2 --noise 0.03

# PUNet 50K
python test.py --dataset PUNet --resol 50000_poisson --denoise_iters 1 --noise 0.01
python test.py --dataset PUNet --resol 50000_poisson --denoise_iters 1 --noise 0.02
python test.py --dataset PUNet --resol 50000_poisson --denoise_iters 2 --noise 0.03

# PCNet 10K
python test.py --dataset PCNet --resol 10000_poisson --denoise_iters 1 --noise 0.01
python test.py --dataset PCNet --resol 10000_poisson --denoise_iters 1 --noise 0.02
python test.py --dataset PCNet --resol 10000_poisson --denoise_iters 2 --noise 0.03

# PCNet 50K
python test.py --dataset PUNet --resol 50000_poisson --denoise_iters 1 --noise 0.01
python test.py --dataset PUNet --resol 50000_poisson --denoise_iters 1 --noise 0.02
python test.py --dataset PUNet --resol 50000_poisson --denoise_iters 2 --noise 0.03

# Ablation study (1)
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 1 --noise 0.01 --ablation1 True
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 1 --noise 0.02 --ablation1 True
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 1 --noise 0.03 --ablation1 True

# Ablation study (1)+iters.
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 1 --noise 0.01 --ablation1 True
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 1 --noise 0.02 --ablation1 True
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 2 --noise 0.03 --ablation1 True

# Ablation study (2): use checkpoint trained by ablation2
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 1 --noise 0.01 --checkpoint ./checkpoints/ablation2_best.pt
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 1 --noise 0.02 --checkpoint ./checkpoints/ablation2_best.pt 
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 2 --noise 0.03 --checkpoint ./checkpoints/ablation2_best.pt

# Ablation study (3)
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 1 --noise 0.01 --ablation3 True
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 1 --noise 0.02 --ablation3 True
python test.py --dataset PUNet --resol 10000_poisson --denoise_iters 2 --noise 0.03 --ablation3 True

Acknowledgement

We implemented the whole pipeline of denoising network with libraries pytorch, pytorch3d, pytorch-cluster and point_cloud_utils. We borrowed datasets, hyper-parameter setting and basic skeleton code of denoising model from authors.

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}
}