PointNet and PointNet++ for Point Cloud Classification and Segmentation
====

This is a reproduction of the papers
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://arxiv.org/abs/1612.00593).
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://arxiv.org/abs/1706.02413).

# Performance

## Classification
| Model           | Dataset    | Metric   | Score - PyTorch  | Score - DGL | Time(s) - PyTorch | Time(s) - DGL |
|-----------------|------------|----------|------------------|-------------|-------------------|---------------|
| PointNet        | ModelNet40 | Accuracy | 89.2(Official)   | 89.3        | 181.8             | 95.0          |
| PointNet++(SSG) | ModelNet40 | Accuracy | 92.4             | 93.3        | 182.6             | 133.7         |
| PointNet++(MSG) | ModelNet40 | Accuracy | 92.8             | 93.3        | 383.6             | 240.5         |

## Part Segmentation

| Model           | Dataset    | Metric   | Score - PyTorch | Score - DGL | Time(s) - PyTorch | Time(s) - DGL |
|-----------------|------------|----------|-----------------|-------------|-------------------|---------------|
| PointNet        | ShapeNet   | mIoU     | 84.3            | 83.6        | 251.6             | 234.0         |
| PointNet++(SSG) | ShapeNet   | mIoU     | 84.9            | 84.5        | 361.7             | 240.1         |
| PointNet++(MSG) | ShapeNet   | mIoU     | 85.4            | 84.6        | 817.3             | 821.8         |

+ Score - PyTorch are collected from [this repo](https://github.com/yanx27/Pointnet_Pointnet2_pytorch).
+ Time(s) are the average training time per epoch, measured on EC2 g4dn.4xlarge instance w/ Tesla T4 GPU.
# How to Run

For point cloud classification, run with

```python
python train_cls.py
```

For point cloud part-segmentation, run with

```python
python train_partseg.py
```

## To Visualize Part Segmentation in Tensorboard
![Screenshot](vis.png)
First ``pip install tensorboard``
then run
```python 
python train_partseg.py --tensorboard
```
To display in Tensorboard, run 
``tensorboard --logdir=runs``