TY - JOUR
T1 - A fast and robust local descriptor for 3D point cloud registration
AU - Yang, Jiaqi
AU - Cao, Zhiguo
AU - Zhang, Qian
N1 - Publisher Copyright:
© 2016 Elsevier Inc. All rights reserved.
PY - 2016/6/10
Y1 - 2016/6/10
N2 - 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.
AB - 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.
KW - 3D point cloud registration
KW - Feature matching
KW - Local feature descriptor
KW - Point correspondences
KW - Self-similar models
UR - http://www.scopus.com/inward/record.url?scp=84964345549&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2016.01.095
DO - 10.1016/j.ins.2016.01.095
M3 - 文章
AN - SCOPUS:84964345549
SN - 0020-0255
VL - 346-347
SP - 163
EP - 179
JO - Information Sciences
JF - Information Sciences
ER -