FoldingNet swMATH ID: 32562 Software Authors: Yaoqing Yang, Chen Feng, Yiru Shen, Dong Tian Description: FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation. 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 Homepage: https://arxiv.org/abs/1712.07262 Dependencies: None Keywords: Computer Vision and Pattern Recognition (cs.CV) Related Software: PointNet; D-Faust; COMA; Adam; PyTorch; AtlasNet; VoxelNet; DeepSDF; NeuralQAAD; Wasserstein GAN; GRAF; KPConv; PointCNN; MeshCNN; t-SNE; EMD; SPLATNet; ScanComplete; PCL; BundleFusion Cited in: 3 Publications all top 5 Cited by 13 Authors 1 Bennamoun, Mohammed 1 Chow, Alix L. H. 1 Derhami, Vali 1 Hua, Jing 1 Komarichev, Artem 1 Long, Chengjiang 1 Rezaei, Masoumeh 1 Rezaeian, Mehdi 1 Sohel, Ferdous A. 1 Xiao, Chunxia 1 Yan, Qingan 1 Zhang, Wenxiao 1 Zhong, Zichun Cited in 1 Serial 3 Computer Aided Geometric Design Cited in 2 Fields 3 Numerical analysis (65-XX) 1 Computer science (68-XX) Citations by Year