A search engine for 3D models

By Thomas A. Funkhouser, P. Min, M. Kazhdan, Joyce Chen, Alex Halderman, David P. Dobkin, D. Jacobs. TOGS, 2003

1,081 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/107a2c807e8aacbcd9afcdeb2ddc9222ac25b15b

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids (2006, methodology): “During the past forty years, a vast number of shape matching and searching techniques have been developed (e.g. using moments, spherical harmonics or Reeb graphs—a recent survey can be found in Iyer et al. [39], see also [30]).”
Sketch-based modeling: A survey (2009, positive): “[63] use the projected contour from 13 different viewpoints to define the shape descriptor of an object, based on their observations that ‘‘people tend to sketch y [from]”
SHREC 2007 3 D Shape Retrieval Contest (2006, methodology): “rank [1,100] for all submitted ranked lists.”
Recognizing Objects in Range Data Using Regional Point Descriptors (2004, background): “Many approaches to 3D object recognition have been put forth, including generalized cylinders [3], superquadrics [7], geons [23], medial axis representations [1], skeletons [4], shape distributions [19], and spherical harmonic representations of global shape [8].”
Sketch2Model: View-Aware 3D Modeling from Single Free-Hand Sketches (2021, background): “[28] categorized traditional sketchbased modeling techniques into evocative [2,9,34] and constructive [17, 19, 25, 26] ones.”
Three-dimensional shape searching: state-of-the-art review and future trends (2005, neutral): “[145].”
A survey of content based 3D shape retrieval methods (2004, methodology): “[30] using a test database containing 1,833 models, at the cost of much more processing time.”
The Princeton Shape Benchmark (2004, methodology): “Moreover, few publications contain results of tests comparing several approaches on the same data [4, 10, 19, 36].”
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition (2013, background): “Examples of these algorithms include the geometric 3D moments (Paquet et al., 2000), shape distribution (Osada et al., 2002) and spherical harmonics (Funkhouser et al., 2003).”
Modeling by example (2004, methodology): “For the whole-object matching experiment, we used the Princeton Shape Benchmark test data set [Shilane et al. 2004] to compare the matching performance of our new descriptor with two state-ofthe-art descriptors: the Spherical Harmonic Representation of the Gaussian Euclidean Distance Transform [Funk”
A planar-reflective symmetry transform for 3D shapes (2006, methodology): “’s planar reflective symmetry descriptor (PRSD) and the Gaussian Euclidean Distance Transform (GEDT), which is currently used in at least one shape based search engine [Funkhouser et al. 2003].”
FFTs on the Rotation Group (2008, methodology): “[11, 19] using these FFTs for the efficient searching of 3D databases — volumetric databases.”
Context-based search for 3D models (2010, methodology): “Several 3D model search engines have been proposed [Funkhouser et al. 2003; Chen et al. 2003].”
Discriminative learning of Markov random fields for segmentation of 3D scan data (2005, methodology): “This is still an open problem in computer vision, where many approaches assume that the scans have been already segmented into objects [10, 6].”