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