Salient geometric features for partial shape matching and similarity

By Ran Gal, Daniel Cohen-Or. 2006

562 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

The wave kernel signature: A quantum mechanical approach to shape analysis (2011, methodology): “Examples include [15, 11, 8], who used ideas similar to the Shape Context in a geodesic distance framework.”
Salient local visual features for shape-based 3D model retrieval (2008, methodology): “As the PSB contains significant number of models that does not satisfy these conditions, the paper by Ran Gal reports results using only a " nicer " subset of the PSB using an undisclosed ground truth classifications.”
Partial and approximate symmetry detection for 3D geometry (2006, methodology): “5 GBytes have been reported for complex geometric shapes [Gal and Cohen-Or 2006].”
3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey (2014, background): “Gal and Cohen-Or [67] proposed a saliency grade for keypoint detection.”
Schelling points on 3D surface meshes (2012, methodology): “[Gal and Cohen-Or 2006] computed the saliency of a region based on its size relative to the whole object, its curvature, the variance of curvature, and the number of curvature changes within the region.”
Reassembling fractured objects by geometric matching (2006, background): “Various descriptors have been proposed including curvatures [Li and Guskov 2005; Gal and Cohen-Or 2006] and integral invariants [Gelfand et al. 2005].”
Contextual Part Analogies in 3D Objects (2010, background): “Nevertheless, partial matching techniques (Belongie et al. 2001; Funkhouser and Shilane 2006; Gal and Cohen-Or 2006; Johnson and Hebert 1999; Novotni et al. 2005; Zhang et al. 2008) are based more on feature correspondence but less on segmentation results.”
Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013, background): “These features include spin images [52], mean curvature [56], Gaussian curvature [53], moments and spherical harmonics [54], integral descriptors [46], [48], [55], fast Fourier transform (FFT) and discrete cosine transform (DCT) coefficients of distributions of normals [17], cluster signatures of si”
3D Mesh Labeling via Deep Convolutional Neural Networks (2015, methodology): “Thus, we ﬁrst extract seven types of geometric features that are widely used in existing studies, including: curvature (CUR) [Gal and Cohen-Or 2006], PCA feature (PCA) [Kalogerakis et al. 2010], shape diameter function (SDF) [Shapira et al. 2010], distance from medial surface (DIS) [Liu et al. 2009]”