dgl/examples/pytorch/pointcloud/pct

• PCT
• Performance
• How to Run

PCT

This is a reproduction of the paper: PCT: Point cloud transformer.

Performance

Task	Dataset	Metric	Score - Paper	Score - DGL (Adam)	Time(s) - DGL
Classification	ModelNet40	Accuracy	93.2	92.1	740.0
Part Segmentation	ShapeNet	mIoU	86.4	85.6	390.0

• Time(s) are the average training time per epoch, measured on EC2 g4dn.12xlarge instance w/ Tesla T4 GPU.
• We run the code with the preprocessing used in PointNet++. We can only get 84.5 for classification if we use the preprocessing described in the paper:

  During training, a random translation in [−0.2, 0.2], a random anisotropic scaling in [0.67, 1.5] and a random input dropout were applied to augment the input data.

How to Run

For point cloud classification, run with

python train_cls.py

For point cloud part-segmentation, run with

python train_partseg.py