A Survey on Shape Correspondence

By Oliver Matias van Kaick, Hao Zhang, Ghassan Hamarneh, Daniel Cohen-Or. 2011

720 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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Citations in the corpus

Dynamic Graph CNN for Learning on Point Clouds (2018, background): “escriptors for point clouds suitable for different problems and data structures. A comprehensive overview of hand-designed point features is out of the scope of this paper, but we refer the reader to [51, 15, 4] for comprehensive discussion. Broadly speaking, one can distinguish between extrinsic an”
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (2016, background): “Modeling and synthesizing 3D shapes 3D object understanding and generation is an important problem in the graphics and vision community, and the relevant literature is very rich [Carlson, 1982, Tangelder and Veltkamp, 2008, Van Kaick et al., 2011, Blanz and Vetter, 1999, Kalogerakis et al., 2012, Ch”
Learning shape correspondence with anisotropic convolutional neural networks (2016, background): “In geometry processing, computer graphics, and vision, finding intrinsic correspondence between 3D shapes affected by different transformations is one of the fundamental problems with a wide spectrum of applications ranging from texture mapping to animation [25].”
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence (2017, background): “Since a detailed review of the literature would be out of scope for this paper, we refer the interested reader to the recent surveys on shape correspondence [39, 6].”
Deformable Shape Completion with Graph Convolutional Autoencoders (2017, background): “We refer the interested reader to [54, 3] for a detailed review of the literature.”
Learning part-based templates from large collections of 3D shapes (2013, methodology): “Several existing approaches address the problem of consistent segmentation and labeling by clustering points in an embedded space of local shape features [Kalogerakis et al. 2010; van Kaick et al. 2011a; Huang et al. 2011; Sidi et al. 2011; Hu et al. 2012; Wang et al. 2012].”
Efficient Globally Optimal 2D-to-3D Deformable Shape Matching (2016, background): “The classical 2D-to2D and 3D-to-3D settings of the shape matching problem have been thoroughly researched in the computer vision and graphics communities (see [33] for a survey).”
Image Matching from Handcrafted to Deep Features: A Survey (2020, methodology): “To generate a more conducive image to subsequent applications, image fusion is adopted to combine the meaningful information from images acquired by different sensors or under different shooting settings (Pohl and Van Genderen 1998), wherein the source images have been accurately aligned in advance.”
Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics (2019, background): “Due to limited scope, we restrict the discussion to only the most relevant works, referring the reader to [6, 60, 66] for a thorough discussion.”
Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering (2011, background): “Shape segmentation [Shamir 2008] and correspondence [van Kaick et al. 2010] are two of the most fundamental problems in high-level shape analysis.”
Product Manifold Filter: Non-rigid Shape Correspondence via Kernel Density Estimation in the Product Space (2017, neutral): “In this setting, research has mainly focused on minimizing a measure of distortion between the input shapes, reaching in recent years very high levels of accuracy [19].”
Bayesian Inference of Bijective Non-Rigid Shape Correspondence (2016, background): “In geometry processing, computer graphics, and vision, estimating correspondence between 3D shapes affected by different transformations is one of the fundamental problems with a wide spectrum of applications ranging from texture mapping to animation [17].”
Factorized Graph Matching (2016, methodology): “In the past decade, ICP has been widely used to solve correspondence problems in image, shape, and surface registration [49] due to its simplicity and low computational complexity.”
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space (2019, background): “Traditionally, systems for such tasks have relied on the approximate computation of geometric features such as faces, edges or corners [31, 11] and hand-crafted features encoding statistical properties [3, 27].”