Contextual Part Analogies in 3D Objects

By Lior Shapira, Shy Shalom, Ariel Shamir, Daniel Cohen-Or, Hao Zhang. 2010

177 citations recorded by the source corpus.

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

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Citations in the corpus

3D Mesh Labeling via Deep Convolutional Neural Networks (2015, background): “In some early studies such as Katz and Tal [2003], Ben-Chen Gotsman [2008], Huang et al. [2009], Shapira et al. [2010], Zhang et al. [2012], and Au et al. [2012], a major concern is to ﬁnd a single feature that is effective for mesh label identiﬁcation.”
3D Shape Segmentation with Projective Convolutional Networks (2016, methodology): “); then, a clustering method or classifier infers part labels for elements based on their descriptors; and finally (optionally) a separate graph cuts step is employed to smooth out the surface labeling [26, 41, 43, 19, 58].”
3D Shape Segmentation and Labeling via Extreme Learning Machine (2014, unknown): “Similarly to [SSS ∗ 10], the smoothness term is deﬁned as: where (cid:96) uv is the length of the incident edge of face u and v , and θ uv is the dihedral angle between the two faces. ω uv is a weighting factor incorporating the boundary constraints based on the boundaries of both over-segmentation…”
Learning part-based templates from large collections of 3D shapes (2013, methodology): “…for data-driven solutions to common geometry processing problems, including surface reconstruction [Kim et al. 2012b], model completion [Shen et al. 2012], model-based object recognition [Shapira et al. 2010; Nan et al. 2012], shape synthesis [Kalogerakis et al. 2012; Zheng et al. 2013], etc.”
Joint shape segmentation with linear programming (2011, unknown): “A related line of work in shape segmentation aims to produce consistent segmentations of multiple shapes, for applications such as part-based 3D modeling [Kraevoy et al. 2007; Golovinskiy and Funkhouser 2009; Shapira et al. 2010; Xu et al. 2010].”
Interactive shape co-segmentation via label propagation (2014, background): “These widely recognized descriptors are the Shape Diameter Function (SDF) [31], the Conformal Factor (CF) [32], the Shape Contexts (SCs) [33,34], the Average Geodesic Distance (AGD) [35], and the geodesic distance to the base of the shape (GB) [7].”
Probabilistic reasoning for assembly-based 3D modeling (2011, methodology): “The feature vectors incorporate descriptors such as shape diameter [Shapira et al. 2010], curvature, shape context [Mori et al. 2001], and PCA-based parameters (Appendix A).”
Learning 3D mesh segmentation and labeling (2010, background): “Most effort has focused on finding simple geometric criteria for segmentation of a single input mesh [Mangan and Whitaker 1999; Shlafman et al. 2002; Katz and Tal 2003; Liu and Zhang 2004; Katz et al. 2005; Simari et al. 2006; Attene et al. 2006b; Lin et al. 2007; Golovinskiy and Funkhouser 2008;…”
Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization (2013, background): “Similar to [5,14], we define the smoothness term as follows,”