Sparse Modeling of Intrinsic Correspondences

By Tobias Gurdan. 2014

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Citations in the corpus

Learning shape correspondence with anisotropic convolutional neural networks (2016, methodology): “This approach was extended in several follow-up works [P ∗ 13,KBBV15,ADK16,RCB ∗ 16] .”
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence (2017, background): “This representation has been adopted and extended in several follow-up works [34, 22, 25, 3, 18, 35, 27, 26, 32], demonstrating state-ofthe-art performance in multiple settings.”
Anisotropic Laplace-Beltrami Operators for Shape Analysis (2014, unknown): “Recent state-of-the-art approaches in deformable shape matching rely on the ability to detect repeatable regions on the given shapes [19].”
Sparse Modeling for Image and Vision Processing (2014, unknown): “…and audio processing [Lewicki, 2002, Elad and Aharon, 2006, Mairal et al., 2009, Yang et al., 2010a] and computer vision [Raina et al., 2007, Yang et al., 2009, Zeiler et al., 2011, Mairal et al., 2012, Song et al., 2012, Castrodad and Sapiro, 2012, Elhamifar et al., 2012, Pokrass et al., 2013].”
Sparse localized deformation components (2013, methodology): “In computer graphics, such methods can help to find robust correspondences between meshes [Pokrass et al. 2013].”
Bayesian Inference of Bijective Non-Rigid Shape Correspondence (2016, methodology): “We follow the approach from [31] to construct a realistic functional map matching, which typically requires region features as input.”
Efficient Globally Optimal 2D-to-3D Deformable Shape Matching (2016, background): “A recent line of works builds upon the functional representation [18, 19, 22, 12, 23], where a point-wise map is replaced by a linear map between function spaces.”
Product Manifold Filter: Non-rigid Shape Correspondence via Kernel Density Estimation in the Product Space (2017, background): “This approach was extended in several follow-up works [33, 22, 3, 35] .”