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[AAAI25] You Should Learn to Stop Denoising on Point Clouds in Advance

ASDN 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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## Introduction
This project is developed based on the paper *"You Should Learn to Stop Denoising on Point Clouds in Advance"*.

## Data
Our data is the same as *Score-Based Point Cloud Denoising* by Shitong Luo and Wei Hu. Kudos to them for their excellent implementation and resources. You can check out their [GitHub repository](https://github.com/luost26/score-denoise). We will also make the data available as a zip file for ease of use. Please download and place it in the `./data` directory.

In addition, we provide a dataset containing four other types of PUNet noise.  
You can download it from [Google Drive](https://drive.google.com/file/d/1XN9OcLWNepGWoxjYQ_6KqZNM8nzxTdib/view?usp=sharing).  
## Environment Setup
To set up the project environment, follow these steps:

```bash
conda create -n ASDN python=3.10
conda activate ASDN
conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=11.8 -c pytorch -c nvidia
conda install lightning=2.4.0 -c conda-forge
conda install iopath=0.1.10 -c conda-forge
conda install pytorch3d::pytorch3d
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.3.1+cu118.html
pip install torch-geometric==2.6.1
pip install point-cloud-utils==0.31.0
pip install pandas
pip install tensorboard

cd Chamfer3D
python setup.py install
cd ..

cd pointops
python setup.py install

```

## Training
To train the proposed ASDN network, run the following command:

```bash
python train_ASDN.py
```

If you want to train the classifier, run:

```bash
python train_classifier.py
```

## Inference
We provide pre-trained models for inference. You can use them by running the following commands:

```bash
python test_ASDN.py --niters 2  --noise_lvls 0.01 
python test_ASDN.py --niters 2  --noise_lvls 0.02
python test_ASDN.py --niters 3  --noise_lvls 0.03
```

ASDN

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ASDN 최신버젼 다운로드

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