Co‐Segmentation of 3D Shapes via Subspace Clustering

By Ruizhen Hu, Lubin Fan, Ligang Liu. 2012

164 citations recorded by the source corpus.

Subfields: Computational Mechanics, Geology, Industrial and Manufacturing Engineering

How researchers see this paper

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

In Computational Mechanics

In Geology

In Industrial and Manufacturing Engineering

Citations in the corpus

3D shape segmentation via shape fully convolutional networks (2017, neutral): “A segmentation model is then obtained and applied to consistent segmentation [36, 37, 38].”
Creating consistent scene graphs using a probabilistic grammar (2014, methodology): “Previous work offers tools for sketchbased scene modeling [Xu et al. 2013], context-based object retrieval [Fisher and Hanrahan 2010], scene retrieval [Fisher et al. 2011], scene organization [Xu et al. 2014], and automatic scene synthesis [Fisher et al. 2012].”
PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding (2018, methodology): “Shape co-segmentation benchmarks [38, 11] were proposed to study co-segmentation among similar shapes.”
A scalable active framework for region annotation in 3D shape collections (2016, background): “…segmented individual shapes [Shamir 2008], several recent works have shown that joint analysis of a group of related objects provides better geometric cues for what constitutes a semantically meaningful part [Sidi et al. 2011; Huang et al. 2011; Hu et al. 2012; Huang et al. 2014; Shu et al. 2016].”
Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization (2013, unknown): “[15] present an unsupervised co-segmentation algorithm that generates the segmentations by grouping the primitive patches of the shapes directly and obtains their correspondences simultaneously.”
Interactive shape co-segmentation via label propagation (2014, methodology): “& 2013 Published by Elsevier Ltd.”
3D Shape Segmentation and Labeling via Extreme Learning Machine (2014, unknown): “On the unsupervised side, clustering methods has been utilized for segmentation and labeling [SvKK ∗ 11, HFL12].”
Active co-analysis of a set of shapes (2012, methodology): “Unsupervised methods for co-segmentation and co-analysis of a set have been presented in [Golovinskiy and Funkhouser 2009; Xu et al. 2010; Sidi et al. 2011; Hu et al. 2012].”
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].”
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].”