期刊
INFORMATION SCIENCES
卷 346, 期 -, 页码 163-179出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2016.01.095
关键词
Local feature descriptor; 3D point cloud registration; Point correspondences; Self-similar models; Feature matching
资金
- National High Technology Research and Development Program of China [2015AA015904]
- China Postdoctoral Science Foundation [2014M562028]
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. (C) 2016 Elsevier Inc. All rights reserved.
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