# Pointnet_Pointnet2_pytorch

**Repository Path**: professor__yang/Pointnet_Pointnet2_pytorch

## Basic Information

- **Project Name**: Pointnet_Pointnet2_pytorch
- **Description**: PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-21
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Pytorch Implementation of PointNet and PointNet++ 

This repo is implementation for [PointNet](http://openaccess.thecvf.com/content_cvpr_2017/papers/Qi_PointNet_Deep_Learning_CVPR_2017_paper.pdf) and [PointNet++](http://papers.nips.cc/paper/7095-pointnet-deep-hierarchical-feature-learning-on-point-sets-in-a-metric-space.pdf) in pytorch.

## Update
**2019/11/26:**

(1) Fixed some errors in previous codes and added data augmentation tricks. Now classification by only 1024 points can achieve 92.8\%! 

(2) Added testing codes, including classification and segmentation, and semantic segmentation with visualization. 

(3) Organized all models into `./models` files for easy using.


## Classification
### Data Preparation
Download alignment **ModelNet** [here](https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip) and save in `data/modelnet40_normal_resampled/`.

### Run
```
## Check model in ./models 
## E.g. pointnet2_msg
python train_cls.py --model pointnet2_cls_msg --normal --log_dir pointnet2_cls_msg
python test_cls.py --normal --log_dir pointnet2_cls_msg
```

### Performance
| Model | Accuracy |
|--|--|
| PointNet (Official) |  89.2|
| PointNet2 (Official) | 91.9 |
| PointNet (Pytorch without normal) |  90.6|
| PointNet (Pytorch with normal) |  91.4|
| PointNet2_SSG (Pytorch without normal) |  92.2|
| PointNet2_SSG (Pytorch with normal) |  92.4|
| PointNet2_MSG (Pytorch with normal) |  **92.8**|

## Part Segmentation
### Data Preparation
Download alignment **ShapeNet** [here](https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip)  and save in `data/shapenetcore_partanno_segmentation_benchmark_v0_normal/`.
### Run
```
## Check model in ./models 
## E.g. pointnet2_msg
python train_partseg.py --model pointnet2_part_seg_msg --normal --log_dir pointnet2_part_seg_msg
python test_partseg.py --normal --log_dir pointnet2_part_seg_msg
```
### Performance
| Model | Inctance avg IoU| Class avg IoU 
|--|--|--|
|PointNet (Official)	|83.7|80.4	
|PointNet2 (Official)|85.1	|81.9	
|PointNet (Pytorch)|	84.3	|81.1|	
|PointNet2_SSG (Pytorch)|	84.9|	81.8	
|PointNet2_MSG (Pytorch)|	**85.4**|	**82.5**	


## Semantic Segmentation
### Data Preparation
Download 3D indoor parsing dataset (**S3DIS**) [here](http://buildingparser.stanford.edu/dataset.html)  and save in `data/Stanford3dDataset_v1.2_Aligned_Version/`.
```
cd data_utils
python collect_indoor3d_data.py
```
Processed data will save in `data/stanford_indoor3d/`.
### Run
```
## Check model in ./models 
## E.g. pointnet2_ssg
python train_semseg.py --model pointnet2_sem_seg --test_area 5 --log_dir pointnet2_sem_seg
python test_semseg.py --log_dir pointnet2_sem_seg --test_area 5 --visual
```
Visualization results will save in `log/sem_seg/pointnet2_sem_seg/visual/` and you can visualize these .obj file by [MeshLab](http://www.meshlab.net/).
### Performance on sub-points of raw dataset (processed by official PointNet [Link](https://shapenet.cs.stanford.edu/media/indoor3d_sem_seg_hdf5_data.zip))
|Model  | Class avg IoU | 
|--|--|
| PointNet (Official) | 41.1|
| PointNet (Pytorch) | 48.9|
| PointNet2 (Official) |N/A | 
| PointNet2_ssg (Pytorch) | **53.2**|
### Performance on raw dataset
still on testing...

## Visualization
### Using show3d_balls.py
```
## build C++ code for visualization
cd visualizer
bash build.sh 
## run one example 
python show3d_balls.py
```
![](/visualizer/pic.png)
### Using MeshLab
![](/visualizer/pic2.png)


## Reference By
[halimacc/pointnet3](https://github.com/halimacc/pointnet3)<br>
[fxia22/pointnet.pytorch](https://github.com/fxia22/pointnet.pytorch)<br>
[charlesq34/PointNet](https://github.com/charlesq34/pointnet) <br>
[charlesq34/PointNet++](https://github.com/charlesq34/pointnet2)

## Environments
Ubuntu 16.04 <br>
Python 3.6.7 <br>
Pytorch 1.1.0