MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Registering sets of points using Bayesian regression
Neurocomputing
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Robotics and Computer-Integrated Manufacturing
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Rigid and non-rigid shape matching for mechanical components retrieval
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
Comparing ICP variants on real-world data sets
Autonomous Robots
3D anatomical shape atlas construction using mesh quality preserved deformable models
Computer Vision and Image Understanding
A facial tracking and transfer method with a key point refinement
ACM SIGGRAPH 2013 Posters
Journal of Mathematical Imaging and Vision
Image and Vision Computing
A 3D geospatial registration service for street level covisualization of 2D vector maps
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
EM-GPA: Generalized Procrustes analysis with hidden variables for 3D shape modeling
Computer Vision and Image Understanding
Robust Bayesian fitting of 3D morphable model
Proceedings of the 10th European Conference on Visual Media Production
Incremental object learning and robust tracking of multiple objects from RGB-D point set data
Journal of Visual Communication and Image Representation
Sparse iterative closest point
SGP '13 Proceedings of the Eleventh Eurographics/ACMSIGGRAPH Symposium on Geometry Processing
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In this paper, we present a unified framework for the rigid and nonrigid point set registration problem in the presence of significant amounts of noise and outliers. The key idea of this registration framework is to represent the input point sets using Gaussian mixture models. Then, the problem of point set registration is reformulated as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized. We show that the popular iterative closest point (ICP) method [1] and several existing point set registration methods [2], [3], [4], [5], [6], [7] in the field are closely related and can be reinterpreted meaningfully in our general framework. Our instantiation of this general framework is based on the the L2 distance between two Gaussian mixtures, which has the closed-form expression and in turn leads to a computationally efficient registration algorithm. The resulting registration algorithm exhibits inherent statistical robustness, has an intuitive interpretation, and is simple to implement. We also provide theoretical and experimental comparisons with other robust methods for point set registration.