FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
This is an implementation for FoldingNet in PyTorch. FoldingNet is a autoencoder for point cloud. As for the details of the paper, please reference on arXiv.
Environment
- Ubuntu 18.04 LTS
- Python 3.8.5
- CUDA 10.1.243
- PyTorch 1.7.0
Reconstruction on ShapeNet
In order to train the model to do the reconstruction, use the command:
python train_ae.py --batch_size <batch_size> --epochs <epochs> --lr <lr> --weight_decay <weight_decay> --num_workers <num_workers>In order to evaluate the model, see the evaluation_ae.py
Transfer Classification on ModelNet40
I train the AutoEncoder on ShapeNet and use the encoder to extract the features of point clouds of training set of ModelNet40. I train a SVM on the features extracted from ModelNet40's training dataset and evaluate the svm on the testing dataset of ModelNet40.
| Accuracy Overall | 79.82% |
|---|---|
| Precision | 92.42% |
| Recall | 81.08% |
| F1-Score | 86.38% |
| category | precision | recall | f1-score | support |
|---|---|---|---|---|
| 0 | 1.0000 | 1.0000 | 1.0000 | 100 |
| 1 | 0.9762 | 0.8200 | 0.8913 | 50 |
| 2 | 0.9400 | 0.9400 | 0.9400 | 100 |
| 3 | 0.6500 | 0.6500 | 0.6500 | 20 |
| 4 | 0.9300 | 0.9300 | 0.9300 | 100 |
| 5 | 0.9574 | 0.9000 | 0.9278 | 100 |
| 6 | 0.8333 | 1.0000 | 0.9091 | 20 |
| 7 | 0.9896 | 0.9500 | 0.9694 | 100 |
| 8 | 0.9896 | 0.9500 | 0.9694 | 100 |
| 9 | 0.9444 | 0.8500 | 0.8947 | 20 |
| 10 | 0.7500 | 0.4500 | 0.5625 | 20 |
| 11 | 0.7778 | 0.7000 | 0.7368 | 20 |
| 12 | 0.7586 | 0.7674 | 0.7630 | 86 |
| 13 | 0.8261 | 0.9500 | 0.8837 | 20 |
| 14 | 0.8000 | 0.7442 | 0.7711 | 86 |
| 15 | 0.0000 | 0.0000 | 0.0000 | 20 |
| 16 | 0.9125 | 0.7300 | 0.8111 | 100 |
| 17 | 1.0000 | 0.9500 | 0.9744 | 100 |
| 18 | 0.9500 | 0.9500 | 0.9500 | 20 |
| 19 | 0.8750 | 0.7000 | 0.7778 | 20 |
| 20 | 1.0000 | 1.0000 | 1.0000 | 20 |
| 21 | 0.9787 | 0.9200 | 0.9485 | 100 |
| 22 | 0.9794 | 0.9500 | 0.9645 | 100 |
| 23 | 0.7812 | 0.5814 | 0.6667 | 86 |
| 24 | 0.9286 | 0.6500 | 0.7647 | 20 |
| 25 | 1.0000 | 0.7200 | 0.8372 | 100 |
| 26 | 0.9153 | 0.5400 | 0.6792 | 100 |
| 27 | 1.0000 | 0.2000 | 0.3333 | 20 |
| 28 | 0.9663 | 0.8600 | 0.9101 | 100 |
| 29 | 0.9167 | 0.5500 | 0.6875 | 20 |
| 30 | 0.9796 | 0.9600 | 0.9697 | 100 |
| 31 | 0.8462 | 0.5500 | 0.6667 | 20 |
| 32 | 0.7692 | 0.5000 | 0.6061 | 20 |
| 33 | 0.8913 | 0.8200 | 0.8542 | 100 |
| 34 | 0.8000 | 0.8000 | 0.8000 | 20 |
| 35 | 1.0000 | 0.9400 | 0.9691 | 100 |
| 36 | 0.9367 | 0.7400 | 0.8268 | 100 |
| 37 | 0.8452 | 0.7100 | 0.7717 | 100 |
| 38 | 1.0000 | 0.5000 | 0.6667 | 20 |
| 39 | 0.7500 | 0.4500 | 0.5625 | 20 |
| micro avg | 0.9242 | 0.8108 | 0.8638 | 2468 |
| macro avg | 0.8786 | 0.7468 | 0.7949 | 2468 |
| weighted avg | 0.9172 | 0.8108 | 0.8542 | 2468 |
| samples avg | 0.8002 | 0.8108 | 0.8037 | 2468 |
Examples
