Probabilistic Boundary-Guided Point Cloud Primitive Segmentation Network
文献类型：期刊论文
| 作者 | Wang, Shaohu2,3,4 ; Qin, Fangbo3,4; Tong, Yuchuang2,3,4; Shang, Xiuqin2,3,4; Zhang, Zhengtao1,2,3,4
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| 刊名 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
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| 出版日期 | 2023 |
| 卷号 | 72页码:13 |
| 关键词 | Boundary prediction primitive segmentation probabilistic representation |
| ISSN号 | 0018-9456 |
| DOI | 10.1109/TIM.2023.3322509 |
| 通讯作者 | Zhang, Zhengtao(zhengtao.zhang@ia.ac.cn) |
| 英文摘要 | Three-dimensional point cloud primitive segmentation aims to segment an original entire point cloud into a set of geometric primitives with different types, which is widely used in the manufacturing industry. The primitive segmentation task is challenging faced with complex shapes and ambiguous boundaries. We observe that the boundary properties of point clouds have not been fully investigated and exploited in previous works, and the primitive segmentation performance is not satisfactory near boundaries, especially gradually-changed boundaries. In this article, we propose a novel probabilistic boundary-guided primitive segmentation (PBPS) network to improve the primitive segmentation ability by emphasizing the boundary cues. First, the point cloud boundary is represented by Gaussian distribution instead of binary representation, which can describe boundaries more informatively and also provides an indication of the ambiguous relationship between point cloud boundaries and inner regions. Second, a probabilistic boundary-guided feature fusion (PBFF) module as well as an instance clustering and type voting strategy are proposed, which process the boundary points and nonboundary points conditioning on different boundary probabilities, to reduce the impact of boundary ambiguity on primitive segmentation. Third, a primitive instance contrastive loss is designed which can relatively loosen the constraints on the distances from boundary points to the centroid in the embedding space. The effectiveness of PBPS was verified by a series of experiments on two computer-aided design (CAD)-based datasets and two real-scene datasets. |
| 资助项目 | National Key Research and Development Program of China[2022YFB3303800] ; National Natural Science Foundation of China[U21A20482] ; National Natural Science Foundation of China[62103413] ; National Natural Science Foundation of China[U1909218] ; National Natural Science Foundation of China[62303457] ; Science Technology Project through the Binzhou Weiqiao Guoke Advanced Technology Research Institute[E2D21710] ; Science Technology Project through the Binzhou Weiqiao Guoke Advanced Technology Research Institute[E2D21711] ; China Postdoctoral Science Foundation[2023M733737] |
| WOS研究方向 | Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:001094454100016 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Science Technology Project through the Binzhou Weiqiao Guoke Advanced Technology Research Institute ; China Postdoctoral Science Foundation |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54448]  |
| 专题 | 脑图谱与类脑智能实验室 中科院工业视觉智能装备工程实验室 |
| 通讯作者 | Zhang, Zhengtao |
| 作者单位 | 1.Binzhou Inst Technol, Binzhou 256601, Shandong, Peoples R China 2.CAS Engn Lab Intelligent Ind Vis, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Shaohu,Qin, Fangbo,Tong, Yuchuang,et al. Probabilistic Boundary-Guided Point Cloud Primitive Segmentation Network[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2023,72:13. |
| APA | Wang, Shaohu,Qin, Fangbo,Tong, Yuchuang,Shang, Xiuqin,&Zhang, Zhengtao.(2023).Probabilistic Boundary-Guided Point Cloud Primitive Segmentation Network.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,72,13. |
| MLA | Wang, Shaohu,et al."Probabilistic Boundary-Guided Point Cloud Primitive Segmentation Network".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 72(2023):13. |
入库方式： OAI收割
来源：自动化研究所
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