Robust Simultaneous 3D Registration via Rank Minimization

We present a robust and accurate 3D registration method for a dense sequence of depth images taken from unknown viewpoints. Our method simultaneously estimates multiple extrinsic parameters of the depth images to obtain a registered full 3D model of the scanned scene. By arranging the depth measurements in a matrix form, we formulate the problem as a simultaneous estimation of multiple extrinsics and a low-rank matrix, which corresponds to the aligned depth images as well as a sparse error matrix. Unlike previous approaches that use sequential or heuristic global registration approaches, our solution method uses an advanced convex optimization technique for obtaining a robust solution via rank minimization. To achieve accurate computation, we develop a depth projection method that has minimum sensitivity to sampling by reading projected depth values in the input depth images. We demonstrate the effectiveness of the proposed method through extensive experiments and compare it with previous standard techniques.