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This is an implementation of the DMRDenoise model based on our paper "Differentiable Manifold Reconstruction for Point Cloud Denoising", which will appear in ACM MM 2020.
- [Aug 15, 2021] Please check out our latest work on point cloud denoising: Score-Based Point Cloud Denoising (ICCV 2021) [Paper] [Code].
The code has been tested with the following environment:
| Package | Version |
|---|---|
| PyTorch | 1.5.1 |
| CUDA and NVCC Compiler | 10.0 |
| scikit-learn | 0.23.1 |
| h5py | 2.10.0 |
| PyTorch Lightning | 0.7.6 |
We provide a yaml file that allows instantaneous environment setup. Run the following command and it will create a conda environment named DMRDenoise with all required packages installed:
conda env create -f environment.yml
conda activate DMRDenoiseNext, compile the EMD operator. Note that this step requires CUDA NVCC compiler:
cd ./ops/emd
python setup.py installYou may also set up the environment manually. We provide the instruction as follows:
# Create a conda environment
conda create --name DMRDenoise python=3.6
conda activate DMRDenoise
# Install required packages
conda install -y pytorch=1.5.1 torchvision=0.6.1 cudatoolkit=9.2 -c pytorch
conda install -y scikit-learn=0.23.1
conda install -y -c conda-forge h5py=2.10.0 pytorch-lightning=0.7.6
# Compile the EMD operator
# [NOTICE] This step requires CUDA NVCC compiler
cd ./ops/emd; python setup.py install; cd ../../The training and testing datasets can be downloaded here. Extract the two zip files into the data folder to use them.
We provide two pretrained models in the pretrained folder. You may use the models to denoise your point clouds:
python denoise.py --input <input_xyz> --output <output_xyz> --ckpt ./pretrained/supervised/epoch=153.ckptIf you have downloaded the datasets, you may barely run python denoise.py to see a quick example.
Before training the model on your own, please make sure the training dataset is prepared.
Supervised training:
python train.pyUnsupervised training:
python train.py --loss_ds None --loss_rec unsupervisedWe provide TensorBoard logs of our pretrained models for reference. To view the logs, run:
tensorboard --logdir ./pretrained
Please cite our paper if you use this code in your own work:
@inproceedings{luo2020differentiable,
title={Differentiable Manifold Reconstruction for Point Cloud Denoising},
author={Luo, Shitong and Hu, Wei},
booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
year={2020},
month={October}
}