A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Registration of point cloud data from a geometric optimization perspective
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Geometry and Convergence Analysis of Algorithms for Registration of 3D Shapes
International Journal of Computer Vision
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
4-points congruent sets for robust pairwise surface registration
ACM SIGGRAPH 2008 papers
OpenGRASP: a toolkit for robot grasping simulation
SIMPAR'10 Proceedings of the Second international conference on Simulation, modeling, and programming for autonomous robots
Stochastic global optimization for robust point set registration
Computer Vision and Image Understanding
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In this paper we propose a new method for pairwise rigid point set registration. We pay special attention to noise robustness, outlier resistance and global optimal alignment. The problem of registering two point clouds in space is converted to a minimization of a nonlinear cost function. We propose a cost function that aims to reduce the impact of noise and outliers. Its definition is based on the input point sets and is directly related to the quality of a concrete rigid transform between them. In order to achieve a global optimal registration, without the need of a good initial alignment, we develop a new stochastic approach for global minimization. Tests on a variety of point sets show that the proposed registration algorithm performs very well on noisy, outlier corrupted and incomplete data.