This is a reproduction of the paper: PCT: Point cloud transformer.
| 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.
For point cloud classification, run with
python train_cls.pyFor point cloud part-segmentation, run with
python train_partseg.py