---
res:
  bibo_abstract:
  - Invariant shape descriptors are instrumental in numerous shape analysis tasks
    including deformable shape comparison, registration, classification, and retrieval.
    Most existing constructions model a 3D shape as a two-dimensional surface describing
    the shape boundary, typically represented as a triangular mesh or a point cloud.
    Using intrinsic properties of the surface, invariant descriptors can be designed.
    One such example is the recently introduced heat kernel signature, based on the
    Laplace-Beltrami operator of the surface. In many applications, however, a volumetric
    shape model is more natural and convenient. Moreover, modeling shape deformations
    as approximate isometries of the volume of an object, rather than its boundary,
    better captures natural behavior of non-rigid deformations in many cases. Here,
    we extend the idea of heat kernel signature to robust isometry-invariant volumetric
    descriptors, and show their utility in shape retrieval. The proposed approach
    achieves state-of-the-art results on the SHREC 2010 large-scale shape retrieval
    benchmark.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Dan
      foaf_name: Raviv, Dan
      foaf_surname: Raviv
  - foaf_Person:
      foaf_givenName: Michael M.
      foaf_name: Bronstein, Michael M.
      foaf_surname: Bronstein
  - foaf_Person:
      foaf_givenName: Alexander
      foaf_name: Bronstein, Alexander
      foaf_surname: Bronstein
      foaf_workInfoHomepage: http://www.librecat.org/personId=58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
    orcid: 0000-0001-9699-8730
  - foaf_Person:
      foaf_givenName: Ron
      foaf_name: Kimmel, Ron
      foaf_surname: Kimmel
  bibo_doi: 10.1145/1877808.1877817
  dct_date: 2010^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/9781450301602
  dct_language: eng
  dct_publisher: ACM@
  dct_title: Volumetric heat kernel signatures@
...