A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information

Remote Sensing

Zhong Xie¹  Mingqiang Guo¹  Zheng Liu¹  Ruina Lv²  Jianguo Chen³  Saishang Zhong³ 

[1] National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China;School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China;State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China;

关键词: rigid registration;    RGB-D information;    correspondence;    point normal filter;    nonconvex optimization;   

DOI  :  10.3390/rs13234755

来源: DOAJ

【 摘 要 】

Rigid registration of 3D indoor scenes is a fundamental yet vital task in various fields that include remote sensing (e.g., 3D reconstruction of indoor scenes), photogrammetry measurement, geometry modeling, etc. Nevertheless, state-of-the-art registration approaches still have defects when dealing with low-quality indoor scene point clouds derived from consumer-grade RGB-D sensors. The major challenge is accurately extracting correspondences between a pair of low-quality point clouds when they contain considerable noise, outliers, or weak texture features. To solve the problem, we present a point cloud registration framework in view of RGB-D information. First, we propose a point normal filter for effectively removing noise and simultaneously maintaining sharp geometric features and smooth transition regions. Second, we design a correspondence extraction scheme based on a novel descriptor encoding textural and geometry information, which can robustly establish dense correspondences between a pair of low-quality point clouds. Finally, we propose a point-to-plane registration technology via a nonconvex regularizer, which can further diminish the influence of those false correspondences and produce an exact rigid transformation between a pair of point clouds. Compared to existing state-of-the-art techniques, intensive experimental results demonstrate that our registration framework is excellent visually and numerically, especially for dealing with low-quality indoor scenes.

【 授权许可】

Unknown