# LCDNet

**Repository Path**: csc105/LCDNet

## Basic Information

- **Project Name**: LCDNet
- **Description**: The official code for Learnable Chamfer Distance for Point Cloud Reconstruction published in Pattern Recognition Letters
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-01-09
- **Last Updated**: 2024-02-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Codes for Learnable Chamfer Distance for Point Cloud Reconstruction

## Environment
* TensorFlow 1.13.1
* Cuda 10.0
* Python 3.6.9
* numpy 1.14.5
* tflearn 0.5.0
* transforms3d

We also provide an available conda environment (`lcd.yaml`) in this repo. Please run:

```
conda env create -f lcd.yaml
```

## Dataset
The adopted ShapeNet Part dataset is adopted following [FoldingNet](http://www.merl.com/research/license#FoldingNet), while the ModelNet10 and ModelNet40 datasets follow [PointNet](https://github.com/charlesq34/pointnet.git).
We also provide the processed datasets in [Google Drive](https://drive.google.com/file/d/1sjUk8o-wsZp2PJUej4TsmjnOPvjJegKR/view?usp=sharing). Please download and unzip the datasets.

Please revise the path by the (`--filepath`) parameter when training or evaluating the networks.
The files in (`--filepath`) should be organized as

        <filepath>
        ├── <trainfile1>.h5 
        ├── <trainfile2>.h5
        ├── ...
        ├── train_files.txt
        └── test_files.txt

where the contents in (`train_files.txt`) or (`test_files.txt`) should include the directory of training or testing h5 files, such as:

        train_files.txt
        ├── <trainfile1>.h5
        ├── <trainfile2>.h5
        ├── ...

## Usage

1. Preparation

```
cd ./tf_ops
bash compile.sh
```

2. Train

For the reconstruction task,
```
Python3 vv_lcd.py
```

Note that the paths of data should be edited through the (`--filepath`) parameter according to your setting. For example, if we use the download dataset (`./objdata/ShapeNet_part`), the training command would be

```
Python3 vv_lcd.py --filepath ./objdata/ShapeNet_part
```


3. Test

For the evaluation of reconstruction errors,
```
Python3 vvae_eva.py
```

The trained weight files should be provided by the (`--savepath`) parameter to evaluate the performances.

Here, we also provide [weights](https://drive.google.com/file/d/1PaKusaibFG72IRmrRXm5oAQ4XEk9Z2lI/view?usp=sharing) of the reconstruction network AE pre-trained on ShapeNet Part dataset. To evaluate its performance, just download and unzip it, then set its folder as savepath#. If the dataset and weights are put in (`./objdata/ShapeNet_part`) and (`./pnae`), respectively, the command would be

```
Python3 vvae_eva.py --filepath ./objdata/ShapeNet_part --savepath ./pnae
```

4.复现信息

复现人：钱毅杰
复现时间：2024.1.31
复现小结：本文提出了一种增强的Chamfer Distance (CD)距离用于点云重构相关网络的训练，其原理在于使用一组网络为CD距离计算过程中点到点的匹配距离动态分配权重，且网络在训练过程中使用对抗策略进行更新以不断关注重构效果较差的区域。相比于原有的CD距离，本方法能够帮助减少重构结果的形状缺陷，而相比于基于学习的方法，本方法具有更快的收敛速度。本方法的缺陷在于加权带来的显存提升，以及对超参数调整的需求。此外，因为方法本身并没有对点到点之间的匹配做改进，因此其仍然会受到匹配算法的限制。