Point Cloud Augmentation with Weighted Local Transformations
Despite the extensive usage of point clouds in 3D vision, relatively limited data are available for training deep neural networks. Although data augmentation is a standard approach to compensate for the scarcity of data, it has been less explored in the point cloud literature. In this paper, we prop...
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| Veröffentlicht in: | Proceedings / IEEE International Conference on Computer Vision S. 528 - 537 |
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01.10.2021
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| Abstract | Despite the extensive usage of point clouds in 3D vision, relatively limited data are available for training deep neural networks. Although data augmentation is a standard approach to compensate for the scarcity of data, it has been less explored in the point cloud literature. In this paper, we propose a simple and effective augmentation method called PointWOLF for point cloud augmentation. The proposed method produces smoothly varying non-rigid deformations by locally weighted transformations centered at multiple anchor points. The smooth deformations allow diverse and realistic augmentations. Furthermore, in order to minimize the manual efforts to search the optimal hyperparameters for augmentation, we present AugTune, which generates augmented samples of desired difficulties producing targeted confidence scores. Our experiments show our framework consistently improves the performance for both shape classification and part segmentation tasks. Particularly, with PointNet++, PointWOLF achieves the state-of-the-art 89.7 accuracy on shape classification with the real-world ScanObjectNN dataset. The code is available at https://github.com/mlvlab/PointWOLF. |
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| AbstractList | Despite the extensive usage of point clouds in 3D vision, relatively limited data are available for training deep neural networks. Although data augmentation is a standard approach to compensate for the scarcity of data, it has been less explored in the point cloud literature. In this paper, we propose a simple and effective augmentation method called PointWOLF for point cloud augmentation. The proposed method produces smoothly varying non-rigid deformations by locally weighted transformations centered at multiple anchor points. The smooth deformations allow diverse and realistic augmentations. Furthermore, in order to minimize the manual efforts to search the optimal hyperparameters for augmentation, we present AugTune, which generates augmented samples of desired difficulties producing targeted confidence scores. Our experiments show our framework consistently improves the performance for both shape classification and part segmentation tasks. Particularly, with PointNet++, PointWOLF achieves the state-of-the-art 89.7 accuracy on shape classification with the real-world ScanObjectNN dataset. The code is available at https://github.com/mlvlab/PointWOLF. |
| Author | Kim, Sihyeon Lee, Sanghyeok Kim, Hyunwoo J. Lee, Jaewon Hwang, Seong Jae Hwang, Dasol |
| Author_xml | – sequence: 1 givenname: Sihyeon surname: Kim fullname: Kim, Sihyeon email: sh_bs15@korea.ac.kr organization: Korea University – sequence: 2 givenname: Sanghyeok surname: Lee fullname: Lee, Sanghyeok email: cat0626@korea.ac.kr organization: Korea University – sequence: 3 givenname: Dasol surname: Hwang fullname: Hwang, Dasol email: dd_sol@korea.ac.kr organization: Korea University – sequence: 4 givenname: Jaewon surname: Lee fullname: Lee, Jaewon email: 2j1ejyu@korea.ac.kr organization: Korea University – sequence: 5 givenname: Seong Jae surname: Hwang fullname: Hwang, Seong Jae email: sjh95@pitt.edu organization: University of Pittsburgh – sequence: 6 givenname: Hyunwoo J. surname: Kim fullname: Kim, Hyunwoo J. email: hyunwoojkim@korea.ac.kr organization: Korea University |
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| Snippet | Despite the extensive usage of point clouds in 3D vision, relatively limited data are available for training deep neural networks. Although data augmentation... |
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| SubjectTerms | grouping and shape Manuals Neural networks Point cloud compression Recognition and classification Segmentation Shape Task analysis Three-dimensional displays Training Vision applications and systems |
| Title | Point Cloud Augmentation with Weighted Local Transformations |
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