Knowledge Commons of Institute of Automation,CAS
Deep Space Probing for Point Cloud Analysis | |
Yirong, Yang; Bin Fan![]() ![]() ![]() | |
2020-12 | |
会议名称 | Proceedings of International Conference on Pattern Recognition |
会议日期 | 2021-1 |
会议地点 | 线上 |
摘要 | 3D points distribute in a continuous 3D space irregularly, thus directly adapting 2D image convolution to 3D points is not an easy job. Previous works often artificially divide the space into regular grids, yet it could be suboptimal to learn geometry. In this paper, we propose SPCNN, namely, Space Probing Convolutional Neural Network, which naturally generalizes image CNN to deal with point clouds. The key idea of SPCNN is learning to probe the 3D space in an adaptive manner. Specifically, we define a pool of learnable convolutional weights, and let each point in the local region learn to choose a suitable convolutional weight from the pool. This is achieved by constructing a geometry guided index-mapping function that implicitly establishes a correspondence between convolutional weights and some local regions in the neighborhood (Fig. 1). In this way, the index-mapping function learns to adaptively partition nearby space for local geometry pattern recognition. With this convolution as a basic operator, SPCNN, a hierarchical architecture can be developed for effective point cloud analysis. Extensive experiments on challenging benchmarks across three tasks demonstrate that SPCNN achieves the state-of-the-art or has competitive performance. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 三维视觉 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44311 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.School of Automation and Electeical Engineering, University of Science and Technology Beijing 4.School of Automation, Hangzhou Dianzi University 5.Beijing Information Science and Technology University |
推荐引用方式 GB/T 7714 | Yirong, Yang,Bin Fan,Yongcheng Liu,et al. Deep Space Probing for Point Cloud Analysis[C],2020. |
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