SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation | |
Fan, Siqi3,4; Dong, Qiulei1,2,3; Zhu, Fenghua4; Lv, Yisheng4; Ye, Peijun4; Wang, Feiyue4 | |
2021-06 | |
会议日期 | 2021-6-19 |
会议地点 | Online |
DOI | 10.1109/CVPR46437.2021.01427 |
页码 | 14499-14508 |
英文摘要 | How to learn effective features from large-scale point clouds for semantic segmentation has attracted increasing attention in recent years. Addressing this problem, we propose a learnable module that learns Spatial Contextual Features from large-scale point clouds, called SCF in this paper. The proposed module mainly consists of three blocks, including the local polar representation block, the dualdistance attentive pooling block, and the global contextual feature block. For each 3D point, the local polar representation block is firstly explored to construct a spatial representation that is invariant to the z-axis rotation, then the dual-distance attentive pooling block is designed to utilize the representations of its neighbors for learning more discriminative local features according to both the geometric and feature distances among them, and finally, the global contextual feature block is designed to learn a global context for each 3D point by utilizing its spatial location and the volume ratio of the neighborhood to the global point cloud. The proposed module could be easily embedded into various network architectures for point cloud segmentation, naturally resulting in a new 3D semantic segmentation network with an encoder-decoder architecture, called SCF-Net in this work. Extensive experimental results on two public datasets demonstrate that the proposed SCF-Net performs better than several state-of-the-art methods in most cases. |
源文献作者 | IEEE ; IEEE Comp Soc ; CVF |
会议录 | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) |
语种 | 英语 |
URL标识 | 查看原文 |
WOS研究方向 | Computer Science ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000742075004070 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48725] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
通讯作者 | Dong, Qiulei; Lv, Yisheng |
作者单位 | 1.Center for Excellence in Brain Science and Intelligence Technology, CAS 2.National Laboratory of Pattern Recognition, CASIA 3.School of Artificial Intelligence, University of Chinese Academy of Sciences 4.State Key Laboratory for Management and Control of Complex Systems, CASIA |
推荐引用方式 GB/T 7714 | Fan, Siqi,Dong, Qiulei,Zhu, Fenghua,et al. SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation[C]. 见:. Online. 2021-6-19. |
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