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
Surface simplification using quadric error metrics
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Understanding belief propagation and its generalizations
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3D brain surface matching based on geodesics and local geometry
Computer Vision and Image Understanding - Special issue on nonrigid image registration
SCAPE: shape completion and animation of people
ACM SIGGRAPH 2005 Papers
Numerical Geometry of Non-Rigid Shapes
Numerical Geometry of Non-Rigid Shapes
Möbius voting for surface correspondence
ACM SIGGRAPH 2009 papers
Deformation-driven shape correspondence
SGP '08 Proceedings of the Symposium on Geometry Processing
Non-rigid registration under isometric deformations
SGP '08 Proceedings of the Symposium on Geometry Processing
International Journal of Computer Vision
The caesar project: a 3-D surface anthropometry survey
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
The farthest point strategy for progressive image sampling
IEEE Transactions on Image Processing
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Correspondence between non-rigid deformable 3D objects provides a foundation for object matching and retrieval, recognition, and 3D alignment. Establishing 3D correspondence is challenging when there are non-rigid deformations or articulations between instances of a class. We present a method for automatically finding such correspondences that deals with significant variations in pose, shape and resolution between pairs of objects.We represent objects as triangular meshes and consider normalized geodesic distances as representing their intrinsic characteristics. Geodesic distances are invariant to pose variations and nearly invariant to shape variations when properly normalized. The proposed method registers two objects by optimizing a joint probabilistic model over a subset of vertex pairs between the objects. The model enforces preservation of geodesic distances between corresponding vertex pairs and inference is performed using loopy belief propagation in a hierarchical scheme. Additionally our method prefers solutions in which local shape information is consistent at matching vertices. We quantitatively evaluate our method and show that is is more accurate than a state of the art method.