Multi-scale binary geometric feature description and matching for accurate registration of point clouds

Siwen Quan; Jie Ma; Fan Feng; Kun Yu

doi:10.1117/12.2540407

31 July 2019 Multi-scale binary geometric feature description and matching for accurate registration of point clouds

Siwen Quan, Jie Ma, Fan Feng, Kun Yu

Proceedings Volume 11198, Fourth International Workshop on Pattern Recognition; 111980L (2019) https://doi.org/10.1117/12.2540407
Event: Fourth International Workshop on Pattern Recognition, 2019, Nanjing, China

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.

Citation Download Citation

Siwen Quan, Jie Ma, Fan Feng, and Kun Yu "Multi-scale binary geometric feature description and matching for accurate registration of point clouds", Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980L (31 July 2019); https://doi.org/10.1117/12.2540407

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