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RSS FeedsRemote Sensing, Vol. 13, Pages 4755: A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information (Remote Sensing)

 
 

24 november 2021 13:29:21

 
Remote Sensing, Vol. 13, Pages 4755: A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information (Remote Sensing)
 


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.


 
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Remote Sensing, Vol. 13, Pages 4758: A New Mapping Function for Spaceborne TEC Conversion Based on the Plasmaspheric Scale Height (Remote Sensing)
Remote Sensing, Vol. 13, Pages 4760: Improving the Spatial Resolution of GRACE-Derived Terrestrial Water Storage Changes in Small Areas Using the Machine Learning Spatial Downscaling Method (Remote Sensing)
 
 
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