Iterative Refinement of Range Images with Anisotropic Error Distribution

We propose a method which refines the range measurement of range finders by computing correspondences of vertices of multiple range images acquired from various viewpoints. Our method assumes that a range image acquired by a laser range finder has anisotropic error distribution which is parallel to the ray direction. Thus, we find corresponding points of range images along with the ray direction. We iteratively converge range images to minimize the distance of corresponding points. We describe the effectiveness of our method by the presenting the experimental results of artificial and real range data. Also we show that our method refines a 3D shape more accurately as opposed to that achieved by using the Gaussian filter.