TY - CHAP A1 - Zinßer, T. A1 - Schmidt, Jochen A1 - Niemann, H. T1 - Performance Analysis of Nearest Neighbor Algorithms for ICP Registration of 3-D Point Sets T2 - Vision, Modeling, and Visualization 2003 N2 - There are many nearest neighbor algorithms tailor made for ICP,but most of them require Special input data like range Images or triangle meshes. We focus on efficient nearest neighbor algorithms that do not impose this limitation, and thus can also be used with 3-D point sets generated by structure-frommotion techniques. We shortly present the evaluated algorithms and introduce the modifications we made to improve their efficiency. In particular, several enhancements to the well-known k-D tree algorithm are described. The first part of our Performance Analysis consists of Experiments on synthetic point sets, whereas the second part features experiments with the ICP algorithm on real point sets. Both parts are completed by a thorough evaluation of the obtained results. KW - ICP Algorithm KW - 3D Point-Sets Y1 - 2003 ER - TY - CHAP A1 - Zinßer, T. A1 - Schmidt, Jochen A1 - Niemann, H. T1 - A Refined ICP Algorithm for Robust 3-D Correspondence Estimation T2 - Proceedings of the IEEE International Conference on Image Processing, volume 2, pages 695-698, Barcelona, Spain N2 - Robust registration of two 3-D point sets is a common problem in computer vision. The iterative closest point (ICP) algorithm is undoubtedly the most popular algorithm for solving this kind of problem. In this paper, we present the Picky ICP algorithm, which has been created by merging several extensions of the standard ICP algorithm, thus improving its robustness and computation time. Using pure 3-D point sets as input data, we do not consider additional information like point color or neighborhood relations. In addition to the standard ICP algorithm and the Picky ICP algorithm proposed in this paper, a robust algorithm due to Masuda and Yokoya and the RICP algorithm by Trucco et al. are evaluated. We have experimentally determined the basin of convergence, robustness to noise and outliers, and computation time of these four ICP based algorithms KW - Iterative closest point algorithm KW - Iterative algorithms KW - Noise robustnes KW - Computer vision Y1 - 2003 ER -