Corpus ID: 21221734

FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds

@article{Yang2017FoldingNetIU,
  title={FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds},
  author={Yaoqing Yang and Chen Feng and Yiru Shen and Dong Tian},
  journal={ArXiv},
  year={2017},
  volume={abs/1712.07262}
}
Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised semantic learning tasks on point clouds such as classification and segmentation. [...] Key Method On the encoder side, a graph-based enhancement is enforced to promote local structures on top of PointNet. Then, a novel folding-based approach is proposed in the decoder, which folds a 2D grid onto the underlying 3D object surface of a point cloud.Expand
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