Research License — FoldingNet

FoldingNet for point cloud auto-encoding.

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.

  •  Yang, Y., Feng, C., Shen, Y., Tian, D., "FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds", arXiv, December 2017.
    BibTeX arXiv Software
    • @article{Yang2017dec,
    • author = {Yang, Yaoqing and Feng, Chen and Shen, Yiru and Tian, Dong},
    • title = {FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds},
    • journal = {arXiv},
    • year = 2017,
    • month = dec,
    • url = {http://arxiv.org/abs/1712.07262}
    • }
  •  Yang, Y., Feng, C., Shen, Y., Tian, D., "FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), DOI: 10.1109/CVPR.2018.00029, June 2018.
    BibTeX TR2018-042 PDF Software
    • @inproceedings{Yang2018jun,
    • author = {Yang, Yaoqing and Feng, Chen and Shen, Yiru and Tian, Dong},
    • title = {FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation},
    • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    • year = 2018,
    • month = jun,
    • doi = {10.1109/CVPR.2018.00029},
    • url = {https://www.merl.com/publications/TR2018-042}
    • }

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