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https://github.com/luost26/dmrdenoise
:snowman: Differentiable Manifold Reconstruction for Point Cloud Denoising (ACM MM 2020)
https://github.com/luost26/dmrdenoise
acm-mm acm-mm-2020 denoising denoising-network point-cloud
Last synced: 4 months ago
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:snowman: Differentiable Manifold Reconstruction for Point Cloud Denoising (ACM MM 2020)
- Host: GitHub
- URL: https://github.com/luost26/dmrdenoise
- Owner: luost26
- License: mit
- Created: 2020-07-26T08:01:16.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-08-15T08:54:51.000Z (over 3 years ago)
- Last Synced: 2023-03-06T15:27:50.411Z (almost 2 years ago)
- Topics: acm-mm, acm-mm-2020, denoising, denoising-network, point-cloud
- Language: Python
- Homepage:
- Size: 5.95 MB
- Stars: 96
- Watchers: 2
- Forks: 13
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DMRDenoise
[[Project Page](https://luost.me/DMRDenoise/)] [[Paper](https://arxiv.org/abs/2007.13551)] [[Code](https://github.com/luost26/DMRDenoise)]
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.
## :loudspeaker: News
- **[Aug 15, 2021]** Please check out our latest work on point cloud denoising: *Score-Based Point Cloud Denoising* (ICCV 2021) [[Paper](https://arxiv.org/abs/2107.10981)] [[Code](https://github.com/luost26/score-denoise)].
## Installation
### Recommended Environment
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 |
### Install via Conda
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:
```bash
conda env create -f environment.yml
conda activate DMRDenoise
```
Next, compile the EMD operator. Note that this step requires CUDA NVCC compiler:
```bash
cd ./ops/emd
python setup.py install
```
### Install Manually
You may also set up the environment manually. We provide the instruction as follows:
```bash
# 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 ../../
```
### Dataset
The training and testing datasets can be downloaded [here](https://drive.google.com/drive/folders/1Qw_bYqsUcekeh165kgRODwIuYpsYIzM1?usp=sharing). Extract the two zip files into the `data` folder to use them.
## Usage
### Denoise
We provide two pretrained models in the `pretrained` folder. You may use the models to denoise your point clouds:
```bash
python denoise.py --input --output --ckpt ./pretrained/supervised/epoch=153.ckpt
```
If you have downloaded the datasets, you may barely run `python denoise.py` to see a quick example.
### Train
Before training the model on your own, please make sure the training dataset is prepared.
Supervised training:
```bash
python train.py
```
Unsupervised training:
```bash
python train.py --loss_ds None --loss_rec unsupervised
```
We provide TensorBoard logs of our pretrained models for reference. To view the logs, run:
```
tensorboard --logdir ./pretrained
```
## Citation
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}
}
```