A survey of content based 3D shape retrieval methods

By Johan W. H. Tangelder, Remco C. Veltkamp. 2004

1,461 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (2016, background): “In the past decades, researchers have made impressive progress on 3D object modeling and synthesis [Van Kaick et al., 2011, Tangelder and Veltkamp, 2008, Carlson, 1982], mostly based on meshes or skeletons.”
Contextual Part Analogies in 3D Objects (2010, background): “Shape matching is also an active topic of research, and numerous shape signatures based on geometry and topology have been proposed (Bustos et al. 2005; Gal et al. 2007; Tangelder and Veltkamp 2008).”
SHOT: Unique signatures of histograms for surface and texture description (2014, background): “more effective than the use of local features, due to the absence of clutter and noise (see [45] for a detailed survey): the aim of this experiment is only to evaluate the relative performance of the considered local features assuming that a local feature approach to shape retrieval has been selecte”
Fast 3D recognition and pose using the Viewpoint Feature Histogram (2010, background): “This has been under investigation for a long time in various research fields, such as computer graphics, robotics, and pattern matching, see [2]–[4] for comprehensive reviews.”
Sketch-based 3D model retrieval using diffusion tensor fields of suggestive contours (2010, background): “With the rapid increase in the number of available 3D models, the ability to accurately and effectively search for 3D model is crucial for many applications such as industrial design, engineering, and manufacturing area.”
SHREC 2007 3 D Shape Retrieval Contest (2006, background): “Each particular representation is suitable to cope with particular application needs.”
Actions as space-time shapes (2005, positive): “Methods for 3D shape analysis and matching have been recently used in computer graphics (see survey in [18]).”
Triplet-Center Loss for Multi-view 3D Object Retrieval (2018, background): “We refer the readers to [34, 13] for a comprehensive survey of 3D shape retrieval.”
Deepshape: Deep learned shape descriptor for 3D shape matching and retrieval (2015, background): “Shape descriptors for shape matching and retrieval have been extensively studied in the geometry community [30, 14, 12, 31, 25].”
Sketch-based shape retrieval (2012, methodology): “While research on example-based retrieval – where users provide a full model as the query – has recently found a lot of interest in the community [Tangelder and Veltkamp 2008], its practical application is difficult, since a good example is often not at hand.”
Actions as Space-Time Shapes (2007, neutral): “Methods for 3D shape analysis and matching have been recently used in computer graphics (see survey in [28]).”