Map-based exploration of intrinsic shape differences and variability
By Raif M. Rustamov, M. Ovsjanikov, Omri Azencot, Mirela Ben-Chen, Frédéric Chazal, Leonidas J. Guibas. 2013
164 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Geometry and Topology
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
- Jonathan Masci
- Davide Boscaini
- M. Ovsjanikov
- Joan Bruna
- Leonidas J. Guibas
- Pierre Vandergheynst
In Computer Graphics and Computer-Aided Design
- Michael M. Bronstein
- Emanuele Rodolà
- Jonathan Masci
- Joan Bruna
- Davide Boscaini
- Hao Li
- Daniel Cremers
- Panos Achlioptas
In Geometry and Topology
- Michael M. Bronstein
- Emanuele Rodolà
- Davide Boscaini
- Jonathan Masci
- Pierre Vandergheynst
- Joan Bruna
- Yann LeCun
- Daniel Cremers
Citations in the corpus
- Geometric Deep Learning: Going beyond Euclidean data (2016, background): “s Riemannian manifolds (surfaces) endowed with properties such as color texture. Even more complex examples include networks of operators, e.g., functional correspondences [3] or difference operators [4] in a collection of 3D shapes, or orientations of overlapping cameras in multi-view vision (“stru”
- Geometric deep learning (2016, background): “Right: in an intrinsic representation, the filter is applied on the surface itself, thus being invariant to deformations. or difference operators [74] in a collection of 3D shapes, or orientations of overlapping cameras in multi-view vision (structure from motion) problems.”
- Anisotropic Diffusion Descriptors (2016, methodology): “Examples of such structures include diffusion distances [CL06, LRF10], global point signatures [Rus07], heat-[SOG09,GBAL09] and wave-kernel [ASC11] signatures, heat kernel maps [OMMG10], stable regions [LBB11, RRBC14], functional maps [O ∗ 12,R ∗ 16], and shape difference operators [R ∗ 13].”
- Learning Representations and Generative Models for 3D Point Clouds (2017, background): “Finding shape analogies has been of interest recently in the geometry processing community (Rustamov et al., 2013; Huang et al., 2018).”