SSF-Align: Point Cloud Registration Based on Statistical Shape Features with Manifold Metric

Abstract

As an important topic in 3D vision, the point cloud registration has been widely used in various applications, including location, reconstruction, and shape recognition. In this paper, we propose a new registration method for this topic, which utilizes statistical shape features (SSFs) and manifold metrics to estimate the transformation matrix. The SSFs are extracted to establish a compact representation for the original point cloud. Then, the representation is mapped into a manifold to reduce the influences of different scales and translations. Finally, the manifold metric is used to minimize the distance based on the compact representation and the pose can be estimated. The advantages of our method include robustness to nonuniform densities, insensitivity to missing parts, and better performance to handle large difference of poses. Experimental results show that our method achieves significant improvements compared to the state of the art methods.