Robust Point Set Registration Using Gaussian Mixture Models

Description

This website hosts implementations of the robust point set registration algorithm described in the paper Robust Point Set Registration Using Gaussian Mixture Models, Bing Jian and Baba C. Vemuri, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8), pp. 1633-1645. (An earlier conference version of this work appeared in the proceedings of ICCV'05 A Robust Algorithm for Point Set Registration Using Mixture of Gaussians, Bing Jian and Baba C. Vemuri.)

Currently we provide implementations in C++, MATLAB, and Python, with full source code. Windows executable, example data sets, and MATLAB demo scripts are available for download.

You can download the source code for free, use and change it as you like, but please refer to this website and cite our papers.

Acknowledgment

This research was in part supported by the National Institutes of Health (NIH RO1 NS046812 and NS42075).
This work has benefited a lot from the code and datasets used in several past related work including:

Haili Chui, Anand Rangarajan: A New Algorithm for Non-Rigid Point Matching. CVPR 2000: 2044-2051 http://www.cis.ufl.edu/~anand/students/chui/research.html
Andrew Fitzgibbon, Robust Registration of 2D and 3D Point Sets. BMVC 2001 http://www.robots.ox.ac.uk/~awf/lmicp/
Yanghai Tsin, Takeo Kanade: A Correlation-Based Approach to Robust Point Set Registration. ECCV (3) 2004: 558-569 http://www.cs.cmu.edu/~ytsin/KCReg/
Andriy Myronenko, Xubo B. Song, Miguel A. Carreira-Perpinan: Non-rigid point set registration: Coherent Point Drift. NIPS 2006: 1009-1016 http://www.csee.ogi.edu/~myron/matlab/cpd/

This program was also tested on some other datasets provided by Adrian Peter, Tibério S. Caetano, Michal Sofka.

Adrian Peter and Anand Rangarajan, "A New Closed-Form Information Metric for Shape Analysis," Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2006.
Tibério S. Caetano, Terry Caelli, Dale Schuurmans, Dante Augusto Couto Barone: Graphical Models and Point Pattern Matching. IEEE Trans. Pattern Anal. Mach. Intell. 28(10): 1646-1663 (2006)
Michal Sofka, Gehua Yang, Charles V. Stewart: Simultaneous Covariance Driven Correspondence (CDC) and Transformation Estimation in the Expectation Maximization Framework. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007

Many thanks go to Santhosh Kodipaka, Ting Chen and Andriy Myronenk for testing this program and providing helpful suggestions.

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Robust Point Set Registration Using Gaussian Mixture Models

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