Joint Registration of Multiple Point Sets with Refinement

The recent advances in fast and affordable 3D scanners, such as the Microsoft Kinect, have triggered new developments in 3D reconstruction. However, the geometric alignment of multiple point clouds is still a very challenging task. This paper addresses the problem of registering multiple point sets by building upon the state-of-the-art Joint Registration of Multiple Point Clouds (JRMPC) algorithm. We advance over JRMPC by incorporating the surface normal orientation of each point in the Gaussian Mixture Models (GMM) employed by this method. We formally derive an expectation-conditional maximization algorithm that iteratively estimates the refined model and transformation parameters. Experiments performed on both synthetic, real indoor and outdoor data prove that incorporating the orientation information substantially improves the parameter estimation, allowing for a 50% average reduction of the registration error compared to JRMPC. Our method is compared to other pairwise approaches, demonstrating the best performance on three different realistic datasets.