# PF-Net-Point-Fractal-Network

**Repository Path**: hchouse/PF-Net-Point-Fractal-Network

## Basic Information

- **Project Name**: PF-Net-Point-Fractal-Network
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-01-12
- **Last Updated**: 2021-01-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PF-Net-Point-Fractal-Network

This repository is still under constructions.

If you have any questions about the code, please email me. Thanks!

This is the Pytorch implement of CVPR2020 PF-Net: Point Fractal Network for 3D Point Cloud Completion. 

https://arxiv.org/abs/2003.00410

##0) Environment
Pytorch 1.0.1
Python 3.7.4

##1) Dataset
```
  cd dataset
  bash download_shapenet_part16_catagories.sh
  You can also download the dataset from 
  链接：https://pan.baidu.com/s/1MavAO_GHa0a6BZh4Oaogug 提取码：3hoe 
```
##2) Train
```
python Train_FPNet.py 
```
Change ‘crop_point_num’ to control the number of missing points.
Change ‘point_scales_list ’to control different input resolutions.
Change ‘D_choose’to control without using D-net.

##3) Evaluate the Performance on ShapeNet
```
python show_recon.py
```
Show the completion results, the program will generate txt files in 'test-examples'.
```
python show_CD.py
```
Show the Chamfer Distances and two metrics in our paper.

##4) Visualization of csv File

We provide some incomplete point cloud in file 'test_one'. Use the following code to complete a incomplete point cloud of csv file:
```
python Test_csv.py
```
change ‘infile’and  ‘infile_real’to select different incomplete point cloud in ‘test_one’

##5) Visualization of Examples

Using Meshlab to visualize  the txt files.