A fast and robust local descriptor for 3D point cloud registration

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• 作者：Jiaqi Yang ^{yjq_hust@163.com" class="auth_mail" title="E-mail the corresponding author} ; Zhiguo Cao ^{zgcao@hust.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Qian Zhang ; ^{hangfanzq@163.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Local feature descriptor ; 3D point cloud registration ; Point correspondences ; Self-similar models ; Feature matching
• 刊名：Information Sciences
• 出版年：2016
• 出版时间：10 June 2016
• 年：2016
• 卷：346-347
• 期：Complete
• 页码：163-179
• 全文大小：3968 K

文摘

This paper proposes a novel local feature descriptor, called a local feature statistics histogram (LFSH), for efficient 3D point cloud registration. An LFSH forms a comprehensive description of local shape geometries by encoding their statistical properties on local depth, point density, and angles between normals. The sub-features in the LFSH descriptor are low-dimensional and quite efficient to compute. In addition, an optimized sample consensus (OSAC) algorithm is developed to iteratively estimate the optimum transformation from point correspondences. OSAC can handle the challenging cases of matching highly self-similar models. Based on the proposed LFSH and OSAC, a coarse-to-fine algorithm can be formed for 3D point cloud registration. Experiments and comparisons with the state-of-the-art descriptors demonstrate that LFSH is highly discriminative, robust, and significantly faster than other descriptors. Meanwhile, the proposed coarse-to-fine registration algorithm is demonstrated to be robust to common nuisances, including noise and varying point cloud resolutions, and can achieve high accuracy on both model data and scene data.