PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds and well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption.

References in zbMATH (referenced in 26 articles )

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  1. Wang, Guangyu; Xu, Gang; Wu, Qing; Wu, Xundong: Two-stage point cloud super resolution with local interpolation and readjustment via outer-product neural network (2021)
  2. Deng, Hao; To, Albert C.: Topology optimization based on deep representation learning (DRL) for compliance and stress-constrained design (2020)
  3. Eliasof, Moshe; Sharf, Andrei; Treister, Eran: Multimodal 3D shape reconstruction under calibration uncertainty using parametric level set methods (2020)
  4. Lang, Xufeng; Sun, Zhengxing: Structure-aware shape correspondence network for 3D shape synthesis (2020)
  5. Liu, Xinhai; Han, Zhizhong; Hong, Fangzhou; Liu, Yu-Shen; Zwicker, Matthias: LRC-net: learning discriminative features on point clouds by encoding local region contexts (2020)
  6. Nicolas Wagner, Ulrich Schwanecke: NeuralQAAD: An Efficient Differentiable Framework for High Resolution Point Cloud Compression (2020) arXiv
  7. Pai, Gautam; Joseph-Rivlin, Mor; Kimmel, Ron; Sochen, Nir: On geometric invariants, learning, and recognition of shapes and forms (2020)
  8. Qian Xie, Yu-Kun Lai, Jing Wu, Zhoutao Wang, Yiming Zhang, Kai Xu, Jun Wang: MLCVNet: Multi-Level Context VoteNet for 3D Object Detection (2020) arXiv
  9. Sun, Xiao; Lian, Zhouhui: EasyMesh: an efficient method to reconstruct 3D mesh from a single image (2020)
  10. Yang, Baorong; Yao, Junfeng; Wang, Bin; Hu, Jianwei; Pan, Yiling; Pan, Tianxiang; Wang, Wenping; Guo, Xiaohu: P2MAT-NET: learning medial axis transform from sparse point clouds (2020)
  11. Zhang, Li-na; Wang, Shi-yao; Zhou, Jun; Liu, Jian; Zhu, Chun-gang: 3D grasp saliency analysis via deep shape correspondence (2020)
  12. Zhang, Wenxiao; Long, Chengjiang; Yan, Qingan; Chow, Alix L. H.; Xiao, Chunxia: Multi-stage point completion network with critical set supervision (2020)
  13. Bashiri, Fereshteh S.; Rostami, Reihaneh; Peissig, Peggy; D’Souza, Roshan M.; Yu, Zeyun: An application of manifold learning in global shape descriptors (2019)
  14. Cheng, Xuan; Zeng, Ming; Lin, Jinpeng; Wu, Zizhao; Liu, Xinguo: Efficient (L_0) resampling of point sets (2019)
  15. Chen, Mingjia; Zou, Qianfang; Wang, Changbo; Liu, Ligang: EdgeNet: deep metric learning for 3D shapes (2019)
  16. Chui, Charles K.; Lin, Shao-Bo; Zhou, Ding-Xuan: Deep neural networks for rotation-invariance approximation and learning (2019)
  17. Hofer, Christoph D.; Kwitt, Roland; Niethammer, Marc: Learning representations of persistence barcodes (2019)
  18. Hu, Siyu; Chen, Xuejin: Preventing self-intersection with cycle regularization in neural networks for mesh reconstruction from a single RGB image (2019)
  19. Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, Juergen Gall: SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences (2019) arXiv
  20. Krishna Murthy Jatavallabhula, Edward Smith, Jean-Francois Lafleche, Clement Fuji Tsang, Artem Rozantsev, Wenzheng Chen, Tommy Xiang, Rev Lebaredian, Sanja Fidler: Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research (2019) arXiv

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