wehub-resource-sync
50 lines
2.1 KiB
Markdown
50 lines
2.1 KiB
Markdown
PointNet and PointNet++ for Point Cloud Classification and Segmentation
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====
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This is a reproduction of the papers
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- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://arxiv.org/abs/1612.00593).
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- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://arxiv.org/abs/1706.02413).
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# Performance
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## Classification
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| Model | Dataset | Metric | Score - PyTorch | Score - DGL | Time(s) - PyTorch | Time(s) - DGL |
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|-----------------|------------|----------|------------------|-------------|-------------------|---------------|
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| PointNet | ModelNet40 | Accuracy | 89.2(Official) | 89.3 | 181.8 | 95.0 |
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| PointNet++(SSG) | ModelNet40 | Accuracy | 92.4 | 93.3 | 182.6 | 133.7 |
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| PointNet++(MSG) | ModelNet40 | Accuracy | 92.8 | 93.3 | 383.6 | 240.5 |
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## Part Segmentation
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| Model | Dataset | Metric | Score - PyTorch | Score - DGL | Time(s) - PyTorch | Time(s) - DGL |
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|-----------------|------------|----------|-----------------|-------------|-------------------|---------------|
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| PointNet | ShapeNet | mIoU | 84.3 | 83.6 | 251.6 | 234.0 |
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| PointNet++(SSG) | ShapeNet | mIoU | 84.9 | 84.5 | 361.7 | 240.1 |
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| PointNet++(MSG) | ShapeNet | mIoU | 85.4 | 84.6 | 817.3 | 821.8 |
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+ Score - PyTorch are collected from [this repo](https://github.com/yanx27/Pointnet_Pointnet2_pytorch).
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+ Time(s) are the average training time per epoch, measured on EC2 g4dn.4xlarge instance w/ Tesla T4 GPU.
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# How to Run
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For point cloud classification, run with
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```python
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python train_cls.py
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```
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For point cloud part-segmentation, run with
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```python
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python train_partseg.py
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```
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## To Visualize Part Segmentation in Tensorboard
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First ``pip install tensorboard``
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then run
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```python
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python train_partseg.py --tensorboard
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```
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To display in Tensorboard, run
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``tensorboard --logdir=runs``
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