Robust and Sparse RGBD Data Registration of Scene Views

Abstract

This paper proposes a complete strategy to optimally filter, enhance and register 3D point clouds captured by commodity RGBD cameras. Starting from the raw data grabbed from multiple viewpoints, we build the scene that gathers all the clouds in one consistent view. The process begins with the innovative adaptation of Kalman filter to Kinect's output. The resulting point cloud is subject to an outlier removal technique and a pre-alignment based on 3D features is performed. Finally, the alignment is refined using Iterative Closest Point (ICP) algorithm. The output of this research work is a consistent 3D model which can be directly used in virtual reality applications, or any 3D rendering process. Test results on real data are presented to validate our approach, and to justify the choice of its different modules.