Anisotropic Diffusion Descriptors

By Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein, Daniel Cremers. 2016

132 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs (2016, methodology): “For comparison, we report the performance of blended maps [24], random forests [38], GCNN [32], ADD [8], and ACNN [7].”
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks (2017, background): “These approaches can be generally related to the ﬁeld of signal processing on graphs (Shuman et al., 2013), extending classical harmonic analysis methods to non-Euclidean domains.”
Geometric Deep Learning: Going beyond Euclidean data (2016, background): “A particular choice of the heat conductivity tensor proposed in [53] is”
Learning shape correspondence with anisotropic convolutional neural networks (2016, methodology): “Analogously to equation (8), the anisotropic heat kernel is given by This construction was used in Anisotropic Diffusion Descrip-tors (ADD) [BMR ∗ 16] to generalize the OSD approach using anisotropic heat kernels (considering the diagonal h αθ t ( x , x ) and learning a set of optimal task-speciﬁc s”
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels (2016, background): “Following the success of Shapenet, there have been recent work that improves the recognition results on 3D data [10], uses 3D-2D(multi-view images) correspondence to improve shape completion (repairing) results [11,12] propose intrinsic CNN [13,14] for 3D data or learn correspondence between two sur”
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence (2017, methodology): “We compare with state-of-the-art learning-based approaches for deformable shape correspondence, namely optimal spectral descriptors (OSD) [28], geodesic CNNs (GCNN) [29], localized spectral CNNs (LSCNN) [8], and two variants of anisotropic diffusion descriptors (ADD3, mADD3) [10].”
Triplet-Center Loss for Multi-view 3D Object Retrieval (2018, methodology): “3D model-based methods directly learn shape features from 3D data formats, such as polygon meshes or surfaces [5, 6, 42, 43], voxel grid [21, 40, 18, 25, 30], and point clouds [24, 26].”
Geometric deep learning (2016, background): “The text in the course materials is primarily based on previously published work including [71, 56, 86, 100, 10, 59, 97, 12, 11] among several others.”
Robust Spatial Filtering With Graph Convolutional Neural Networks (2017, background): “Some example representations are 2D polar coordinate representations that are filtered with a polar convolution [27], local windowed spectral representations [28], anisotropic variants of heat kernel diffusion filters and spectral filters [29], [30], and learned Gaussian Mixture-Model kernels [31].”
3D-CODED: 3D Correspondences by Deep Deformation (2018, methodology): “To improve correspondence quality, these methods have been extended to take advantage of category-specific data priors [9].”
CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters (2017, background): “Geometric deep learning approaches have been successfully applied to computer graphics and vision [22, 2, 4, 3, 24], brain imaging [19], and drug design [11] problems, to mention a few.”