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Multi-scale binary geometric feature description and matching for accurate registration of point clouds
Author(s): Siwen Quan; Jie Ma; Fan Feng; Kun Yu
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Paper Abstract

Point cloud registration in military scenarios is pivotal to automatic object reconstruction and recognition. This paper proposes 1) a multi-scale binary feature representation called mLoVS (multi-scale local voxelized structure) and 2) a “min-pooling” based feature matching technique for accurate registration of tank point clouds. The key insight of our method is that traditional fixed-scale feature matching methods either suffer from limited shape information or data missing caused by occlusion, while the multi-scale way provides a flexible matching choice. In addition, the binary nature of our feature representation can alleviate the increased time budget required by multi-scale feature matching. Experiments on several sets of tank point clouds confirm the effectiveness and overall superiority of our method.

Paper Details

Date Published: 31 July 2019
PDF: 5 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980L (31 July 2019); doi: 10.1117/12.2540407
Show Author Affiliations
Siwen Quan, Huazhong Univ. of Science and Technology (China)
Jie Ma, Huazhong Univ. of Science and Technology (China)
Fan Feng, Huazhong Univ. of Science and Technology (China)
Kun Yu, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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