FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation

Yaoqing Yang, Chen Feng, Yiru Shen, Dong Tian; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 206-215


Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation. In this work, a novel end-to-end deep auto-encoder is proposed to address unsupervised learning challenges on point clouds. On the encoder side, a graph-based enhancement is enforced to promote local structures on top of PointNet. Then, a novel folding-based decoder deforms a canonical 2D grid onto the underlying 3D object surface of a point cloud, achieving low reconstruction errors even for objects with delicate structures. The proposed decoder only uses about 7% parameters of a decoder with fully-connected neural networks, yet leads to a more discriminative representation that achieves higher linear SVM classification accuracy than the benchmark. In addition, the proposed decoder structure is shown, in theory, to be a generic architecture that is able to reconstruct an arbitrary point cloud from a 2D grid. Our code is available at

Related Material

[pdf] [Supp]
author = {Yang, Yaoqing and Feng, Chen and Shen, Yiru and Tian, Dong},
title = {FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}