@inproceedings{18343,
  abstract     = {In this paper, we explore the use of the diffusion geometry framework for the fusion of geometric and photometric information in local heat kernel signature shape descriptors. Our construction is based on the definition of a diffusion process on the shape manifold embedded into a high-dimensional space where the embedding coordinates represent the photometric information. Experimental results show that such data fusion is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fail.},
  author       = {Kovnatsky, Artiom and Bronstein, Michael M. and Bronstein, Alexander and Kimmel, Ron},
  booktitle    = {3rd International Conference on Scale Space and Variational Methods in Computer Vision},
  isbn         = {9783642247842},
  issn         = {1611-3349},
  location     = {Ein-Gedi, Israel},
  pages        = {616--627},
  publisher    = {Springer Nature},
  title        = {{Photometric heat kernel signatures}},
  doi          = {10.1007/978-3-642-24785-9_52},
  volume       = {6667},
  year         = {2012},
}