Robust 3D Shape Correspondence in the Spectral Domain

By Varun Jain, Hao Zhang. 2006

180 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Geology

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Citations in the corpus

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling (2018, methodology): “In contrast, metrics like shortest distance and geodesic distance are more commonly used than IoU for matching meshes in graphics [31, 25].”
Dense Human Body Correspondences Using Convolutional Networks (2015, background): “A recent focus is on spectral shape embedding and induced descriptors [17, 39, 30, 5, 28].”
Salient local visual features for shape-based 3D model retrieval (2008, methodology): “An advantage of the BF- SIFT over the method by Jain, et al is its capability to accept diverse shape representations, including polygon soup, point set, and B-rep solids.”
Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013, methodology): “1), the correspondence problem (h 2 H) reduces to a standard rigid or low-dimensional nonrigid matching and alignment problem (f 2 F ) [36], [103].”
Model Composition from Interchangeable Components (2007, background): “Others compute complete point-to-point correspondences [2, 14 , 8]. Most feature based methods are robust only under rigid transformations.”
Laplace-Beltrami eigenfunctions for deformation invariant shape representation (2007, unknown): “We should also mention that in practice there are two problems while working with eigenvalues and eigenvectors in general [JZ06]: the signs of eigenvectors are undeﬁned, and two eigenvectors may be swapped.”
Reeb graphs for shape analysis and applications (2008, background): “Although we believe in the potential of Laplacian eigenfunctions as shape descriptors, there is a number of questions that should be addressed in order to use them for shape retrieval purposes, such as the problem of eigenfunction switching [79].”
Technical Section: Discrete Laplace-Beltrami operators for shape analysis and segmentation (2009, background): “correspondence [27], matching and retrieval [28,49,52], manifold learning [5], and imaging or medical imaging applications [44,38,50].”