IS = { zkontrolovano 25 Jan 2014 },
  UPDATE  = { 2014-01-06 },
  author       = {Sharma, Avinash and Horaud, Radu P. and Cech, Jan and Boyer, Edmond},
  title        = {Topologically-Robust 3D Shape Matching Based on Diffusion Geometry and Seed Growing},
  booktitle    = {Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages        = {2481--2488},
  book_pages =  {3503},
  day          = {20--25},
  month        = {June},
  year         = {2011},
  publisher    = {IEEE Computer Society Press},
  address       = {Los Alamitos, USA},
  ISBN         = {978-1-4577-0394-2},
  venue        = {Colorado Springs, CO, USA},
  doi = {10.1109/CVPR.2011.5995455},
  url          = {http://perception.inrialpes.fr/Publications/2011/SHCB11},
  ANNOTE       = {3D Shape matching is an important problem in
                  computer vision. One of the major difficulties in
                  finding dense correspondences between 3D shapes is
                  related to the topological discrepancies that often
                  arise due to complex kinematic motions. In this
                  paper we propose a shape matching method that is
                  robust to such changes in topology. The algorithm
                  starts from a sparse set of seed matches and outputs
                  dense matching. We propose to use a shape descriptor
                  based on properties of the heat-kernel and which
                  provides an intrinsic scale-space
                  representation. This descriptor incorporates (i)
                  heat-flow from already matched points and (ii) self
                  diffusion. At small scales the descriptor behaves
                  locally and hence it is robust to global changes in
                  topology. Therefore, it can be used to build a
                  vertex-to-vertex matching score conditioned by an
                  initial correspondence set. This score is then used
                  to iteratively add new correspondences based on a
                  novel seed-growing method that iteratively
                  propagates the seed correspondences to nearby
                  vertices. The matching is farther densified via an
                  EM-like method that explores the congruency between
                  the two shape embeddings. Our method is compared
                  with two recently proposed algorithms and we show
                  that we can deal with substantial topological
                  differences between the two shapes.},
  keywords     = {shape descriptor, matching, 3D mesh, topology, artifacts, seed growing},