@inproceedings{18380,
  abstract     = {This work presents a novel approach for detecting inliers in a given set of correspondences (matches). It does so without explicitly identifying any consensus set, based on a method for inlier rate estimation (IRE). Given such an estimator for the inlier rate, we also present an algorithm that detects a globally optimal transformation. We provide a theoretical analysis of the IRE method using a stochastic generative model on the continuous spaces of matches and transformations. This model allows rigorous investigation of the limits of our IRE method for the case of 2D-translation, further giving bounds and insights for the more general case. Our theoretical analysis is validated empirically and is shown to hold in practice for the more general case of 2D-affinities. In addition, we show that the combined framework works on challenging cases of 2D-homography estimation, with very few and possibly noisy inliers, where RANSAC generally fails.},
  author       = {Litman, Roee and Korman, Simon and Bronstein, Alexander and Avidan, Shai},
  booktitle    = {2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  isbn         = {9781467369640},
  issn         = {1063-6919},
  location     = {Boston, MA, United States},
  publisher    = {IEEE},
  title        = {{Inverting RANSAC: Global model detection via inlier rate estimation}},
  doi          = {10.1109/cvpr.2015.7299161},
  year         = {2015},
}

@inproceedings{18378,
  abstract     = {In this work, we present intrinsic shape context (ISC) descriptors for 3D shapes. We generalize to surfaces the polar sampling of the image domain used in shape contexts: for this purpose, we chart the surface by shooting geodesic outwards from the point being analyzed; `angle' is treated as tantamount to geodesic shooting direction, and radius as geodesic distance. To deal with orientation ambiguity, we exploit properties of the Fourier transform. Our charting method is intrinsic, i.e., invariant to isometric shape transformations. The resulting descriptor is a meta-descriptor that can be applied to any photometric or geometric property field defined on the shape, in particular, we can leverage recent developments in intrinsic shape analysis and construct ISC based on state-of-the-art dense shape descriptors such as heat kernel signatures. Our experiments demonstrate a notable improvement in shape matching on standard benchmarks.},
  author       = {Kokkinos, I. and Bronstein, M. M. and Litman, R. and Bronstein, Alexander},
  booktitle    = {2012 IEEE Conference on Computer Vision and Pattern Recognition},
  isbn         = {9781467312264},
  issn         = {1063-6919},
  location     = {Providence, RI, United States},
  publisher    = {IEEE},
  title        = {{Intrinsic shape context descriptors for deformable shapes}},
  doi          = {10.1109/cvpr.2012.6247671},
  year         = {2012},
}

@inproceedings{18379,
  abstract     = {We consider the problem of minimum distortion intrinsic correspondence between deformable shapes, many useful formulations of which give rise to the NP-hard quadratic assignment problem (QAP). Previous attempts to use the spectral relaxation have had limited success due to the lack of sparsity of the obtained “fuzzy” solution. In this paper, we adopt the recently introduced alternative L 1 relaxation of the QAP based on the principles of game theory. We relate it to the Gromov and Lipschitz metrics between metric spaces and demonstrate on state-of-the-art benchmarks that the proposed approach is capable of finding very accurate sparse correspondences between deformable shapes.},
  author       = {Rodola, E. and Bronstein, Alexander and Albarelli, A. and Bergamasco, F. and Torsello, A.},
  booktitle    = {2012 IEEE Conference on Computer Vision and Pattern Recognition},
  isbn         = {978-1-4673-1226-4},
  issn         = {1063-6919},
  location     = {Providence, RI, United States},
  publisher    = {IEEE},
  title        = {{A game-theoretic approach to deformable shape matching}},
  doi          = {10.1109/cvpr.2012.6247674},
  year         = {2012},
}

@inproceedings{18377,
  abstract     = {We introduce an (equi-)affine invariant diffusion geometry by which surfaces that go through squeeze and shear transformations can still be properly analyzed. The definition of an affine invariant metric enables us to construct an invariant Laplacian from which local and global geometric structures are extracted. Applications of the proposed framework demonstrate its power in generalizing and enriching the existing set of tools for shape analysis.},
  author       = {Raviv, Dan and Bronstein, Michael M. and Bronstein, Alexander and Kimmel, Ron and Sochen, Nir},
  booktitle    = {CVPR 2011},
  isbn         = {9781457703942},
  issn         = {1063-6919},
  location     = {Colorado Springs, CO, United States},
  publisher    = {IEEE},
  title        = {{Affine-invariant diffusion geometry for the analysis of deformable 3D shapes}},
  doi          = {10.1109/cvpr.2011.5995486},
  year         = {2011},
}