Perceiving Systems Conference Paper 2011

Shape and pose-invariant correspondences using probabilistic geodesic surface embedding

Dagm2011imagesmall

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.

Author(s): Tsoli, A. and Black, M. J.
Book Title: 33rd Annual Symposium of the German Association for Pattern Recognition (DAGM)
Volume: 6835
Pages: 256--265
Year: 2011
Series: Lecture Notes in Computer Science
Editors: Mester, Rudolf and Felsberg, Michael
Publisher: Springer
Project(s):
Bibtex Type: Conference Paper (inproceedings)
Electronic Archiving: grant_archive
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BibTex

@inproceedings{Tsoli:DAGM:11,
  title = {Shape and pose-invariant correspondences using probabilistic geodesic surface embedding},
  booktitle = {33rd Annual Symposium of the German Association for Pattern Recognition (DAGM)},
  abstract = {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.},
  volume = {6835},
  pages = {256--265},
  series = {Lecture Notes in Computer Science},
  editors = {Mester, Rudolf and Felsberg, Michael},
  publisher = {Springer},
  year = {2011},
  slug = {tsoli-dagm-11},
  author = {Tsoli, A. and Black, M. J.}
}