A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching
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Publication:408814
DOI10.1007/s11263-009-0301-6zbMath1477.68334OpenAlexW1982465050MaRDI QIDQ408814
Ron Kimmel, Alexander M. Bronstein, Mona Mahmoudi, Guillermo Sapiro, Michael M. Bronstein
Publication date: 12 April 2012
Published in: International Journal of Computer Vision (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/11299/180105
Computer graphics; computational geometry (digital and algorithmic aspects) (68U05) Machine vision and scene understanding (68T45)
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Cites Work
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- Topology-invariant similarity of nonrigid shapes
- On reconstructing \(n\)-point configurations from the distribution of distances or areas
- An efficient solution to the eikonal equation on parametric manifolds
- A discrete Laplace-Beltrami operator for simplicial surfaces
- Diffusion maps
- A theoretical and computational framework for isometry invariant recognition of point cloud data
- O(\(N\)) implementation of the fast marching algorithm
- Geometric and Photometric Data Fusion in Non-Rigid Shape Analysis
- Manifold parametrizations by eigenfunctions of the Laplacian and heat kernels
- Numerical Geometry of Non-Rigid Shapes
- Shape distributions
- Efficient Computation of Isometry‐Invariant Distances Between Surfaces
- A Best Possible Heuristic for the k-Center Problem
- Computing geodesic paths on manifolds
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Distance Functions and Geodesics on Submanifolds of $\R^d$ and Point Clouds
- Generalized multidimensional scaling: A framework for isometry-invariant partial surface matching
- Fast computation of weighted distance functions and geodesics on implicit hyper-surfaces
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