Real-time point cloud registration for flexible hand-held 3D scanning

In this paper, we propose a method of real-time point cloud registration for flexible hand-held 3D scanning. In this study, The problem of point cloud registration to be solved can be divided into refined registration and coarse registration with eight small or large overlap. The fine registration problem is solved by point-to-projection algorithm to ensure high efficiency. In addition, we solve the two types of coarse registration by exhaustive screening with different sampling means. To employ sampling screening algorithm, first we establish multiple matching relationships between two range image by using sampling point pairs, which are derived from the sampling sets of the respective 3D point clouds. Then we propose pose evaluation algorithm(PEA) inspired by ICP to screen out the most optimal matching relationship as the coarse registration result. In this case, we design PEA as a separate kernel function combined with GPU parallel technology to realize real-time computing. Back-projection calibration technology that robust for system distance error solve the problem of pose rejection criteria. The algorithm is highly versatile and robust, since the feature information of the 3D point cloud has never been utilized and extracted. The proposed method has been applied to our hand-held 3D scanners and has been tested on extensive real measured data to demonstrate the effectiveness.