This repo is the MindSpore implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)(https://arxiv.org/pdf/1801.07829). Our code skeleton is borrowed from WangYueFt/dgcnn.
- Ubuntu 20.04
- Python 3.7
- MindSpore 1.8.1
- CUDA 11.1
- Package: h5py, sklearn, plyfile
python dgcnn_cls_train.py
python dgcnn_cls_eval.py
ModelNet40 dataset
Mean Class Acc | Overall Acc | |
---|---|---|
Paper (1024 points) | 90.2 | 92.9 |
This repo (1024 points) |
python dgcnn_seg_train.py
python dgcnn_seg_eval.py
ShapeNet part dataset
Mean IoU | Airplane | Bag | Cap | Car | Chair | Earphone | Guitar | Knife | Lamp | Laptop | Motor | Mug | Pistol | Rocket | Skateboard | Table | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Shapes | 2690 | 76 | 55 | 898 | 3758 | 69 | 787 | 392 | 1547 | 451 | 202 | 184 | 283 | 66 | 152 | 5271 | |
Paper | 85.2 | 84.0 | 83.4 | 86.7 | 77.8 | 90.6 | 74.7 | 91.2 | 87.5 | 82.8 | 95.7 | 66.3 | 94.9 | 81.1 | 63.5 | 74.5 | 82.6 |
This repo |
We thank a lot for the official repo of WangYueFt/dgcnn.
The code is released under MIT License(See LICENSE file for details)