Abstract

The coarse-to-fine method is the prime technology for point cloud registration in 3D reconstruction. Aimed at the problem of low accuracy of coarse registration for the partially overlapping point clouds, a novel, to the best of our knowledge, 3D local feature descriptor named grid normals deviation angles statistics (GNDAS) for aligning roughly pairwise point clouds is proposed in this paper. The descriptor is designed by first dividing evenly the local surface into some grids along the $x$ axis and $y$ axis of the local reference frame, then making the statistics of the deviation angles of normals at grid points. Based on the correspondences generated by matching descriptors and a transformation estimation method, the initial registration result is obtained. The coarse registration result is used as the initial value of the iterative closest point algorithm to achieve the refinement of the registration result. Experimental comparisons on two public datasets demonstrate that our GNDAS descriptor has high descriptiveness and strong robustness to noise at low level and varying mesh resolution. The registration results also confirm the superiority of our registration approach over previous versions in accuracy and efficiency.

© 2021 Optical Society of America

ICP registration with DCA descriptor for 3D point clouds

Ying He, Jun Yang, Xingming Hou, Shiyan Pang, and Jia Chen
Opt. Express 29(13) 20423-20439 (2021)

Efficient and accurate registration with BWPH descriptor for low-quality point clouds

Zhihua Du, Yong Zuo, Xiaohan Song, Yuhao Wang, Xiaobin Hong, and Jian Wu
Opt. Express 31(23) 39307-39322 (2023)

Multi-level height maps-based registration method for sparse LiDAR point clouds in an urban scene

Bin Fang, Jie Ma, Pei An, Zhao Wang, Jun Zhang, and Kun Yu
Appl. Opt. 60(14) 4154-4164 (2021)

PDC-Net: Robust point cloud registration using deep cyclic neural network combined with PCA

Dengzhi Liu, Yu Zhang, Lin Luo, Jinlong Li, and Xiaorong Gao
Appl. Opt. 60(11) 2990-2997 (2021)

Hierarchical registration of laser point clouds between airborne and vehicle-borne data considering building eave attributes

Yadong Guo, Xiankun Wang, Dianpeng Su, Fanlin Yang, Guoyu Li, and Chao Qi
Appl. Opt. 60(15) C20-C31 (2021)