Technical Section: Discrete Laplace-Beltrami operators for shape analysis and segmentation
By Martin Reuter, Silvia Biasotti, Daniela Giorgi, Giuseppe Patanè, Michela Spagnuolo. 2009
355 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/aacb63c3ce287c27d9b1aee26bd05e289465350d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Michael M. Bronstein
- Emanuele Rodolà
- Daniel Cremers
- Mathieu Aubry
- Davide Boscaini
- Jonathan Masci
- A. Bronstein
- Iasonas Kokkinos
In Computer Vision and Pattern Recognition
- Michael M. Bronstein
- Luc Van Gool
- Emanuele Rodolà
- Dacheng Tao
- Daniel Cremers
- Ming-Hsuan Yang
- Jitendra Malik
- Andrea Vedaldi
Citations in the corpus
- Spatial adaption of robot trajectories based on laplacian trajectory editing (2015, methodology): “The Laplace-Beltrami operatoris well-known in the computer graphics community where it is applied to deform (Botsch and Kobbelt (2004a); Sorkine and Cohen-Or (2004)), classify (Luxburg (2007); Reuter et al (2009)) and compress (Karni and Gotsman (2000); Levy (2006)) triangular surfaces meshes.”
- Scale-invariant heat kernel signatures for non-rigid shape recognition (2010, methodology): “Reuter et al. [33, 32 ] used the Laplacian spectra as intrinsic shape descriptors.”
- Anisotropic Laplace-Beltrami Operators for Shape Analysis (2014, background): “Computing the LB operator directly on meshes with approaches such as [18, 15, 20], numerous works exploit the information contained in its eigen-decomposition to build a new representation of a shape, either by explicit formulas [23, 24, 1] or by learning [13, 22].”