dgl/examples/pytorch/pointcloud/pointnet

• PointNet and PointNet++ for Point Cloud Classification and Segmentation
• Performance
• • Classification
  • Part Segmentation
• How to Run
• • To Visualize Part Segmentation in Tensorboard

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.
• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space.

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.
• 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 train_cls.py

For point cloud part-segmentation, run with

python train_partseg.py

To Visualize Part Segmentation in Tensorboard

First pip install tensorboard
then run

python train_partseg.py --tensorboard

To display in Tensorboard, run
tensorboard --logdir=runs