Abstract
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.
Highlights
In recent years, 3D point clouds of indoor scenes have been regarded as the most appropriate data source for generating a building information model (BIM), which has become a crucial tool for constructions and architecture professionals [1]
Indoor scene point clouds were usually acquired by static terrestrial laser scanners (TLS)
We present a new rigid registration framework to effectively deal with RGB-D point clouds
Summary
3D point clouds of indoor scenes have been regarded as the most appropriate data source for generating a building information model (BIM), which has become a crucial tool for constructions and architecture professionals [1]. Indoor scene point clouds were usually acquired by static terrestrial laser scanners (TLS). TLS have a high scanning precision, they are expensive. Has allowed ordinary people to capture 3D indoor scene point clouds from the real world by scanning and reconstruction processes [2]. As a fundamental task, 3D point clouds registration aims at registering individual scans in a unified coordinate system for producing a complete 3D point cloud of the target indoor scene [1,3].
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