Global Refinement Algorithm for 3D Scene Reconstruction from a Sequence of Point Clouds

Abstract

Point cloud registration is a central problem in many computer vision problems. However, ensuring global consistency of the results of pairwise registration of point clouds is still a challenge when there are multiple clouds because different scans should be converted to a common coordinate system. This paper describes a global refinement algorithm that first estimates rotations and then estimates parallel translations. For global refinement of rotations, a closed-form algorithm based on matrices is used. For global refinement of parallel translations, a closed-form algorithm is also used. The proposed algorithm is compared with other global refinement algorithms.