Meta-representation of shape families

By Qi-Xing Huang, Fan Wang, Leonidas J. Guibas. 2014

202 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

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Citations in the corpus

SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation (2016, methodology): “Inspired by the recent work on synchronization [23, 25, 26, 9], we propose to align these different spectral domains using functional map [21].”
Picture: A probabilistic programming language for scene perception (2015, methodology): “Procedural graphics techniques [2, 10] would support far more complex object and scene models [50, 14, 7, 15].”
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence (2017, background): “This representation has been adopted and extended in several follow-up works [34, 22, 25, 3, 18, 35, 27, 26, 32], demonstrating state-ofthe-art performance in multiple settings.”
Sparse Representation for 3D Shape Estimation: A Convex Relaxation Approach (2015, background): “[56], [57]) and databases with rich annotations (e.”
3D Shape Segmentation with Projective Convolutional Networks (2016, methodology): “Other approaches segment shapes by employing non-rigid alignment steps through deformable part templates [27, 20], or transfer labels through surface correspondences and functional maps between 3D shapes [48, 22, 50, 27, 23].”
Learning Semantic Deformation Flows with 3D Convolutional Networks (2016, methodology): “These methods aim to decipher the geometric principles that underlie a product family in order to enable deformers that are customized for individual models, thereby expanding data-driven techniques beyond compositional modeling [46, 12, 44].”
Learning Shape Abstractions by Assembling Volumetric Primitives (2016, methodology): “Pre-labeled, part-based shape representations were used for capturing the categoryspecific shape manifold [8], as well as generating novel objects [19, 17] or recovering 3D from 2.”
Deformation-driven shape correspondence via shape recognition (2017, methodology): “Parallel works explored the use of part-based Bayesian networks [Chaudhuri et al. 2011; Kalogerakis et al. 2012] and modular templates [Kim et al. 2013; Fish et al. 2014] to represent both continuous and discrete variations.”
PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation (2018, background): “Further, functional map and cycle consistency [9, 10] helps build correspondence between shapes.”
A scalable active framework for region annotation in 3D shape collections (2016, methodology): “While earlier approaches 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. ”
Learning Shape Templates With Structured Implicit Functions (2019, background): “Part Segmentation: Others have studied how to decompose mesh collections into consistent sets of semantic parts, either through geometric [14] or learned methods [1, 12, 21, 27].”
PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning (2021, background): “Restricted by the annotations in existing large-scale datasets, previous part-based 3D understanding works mainly focus on identifying parts [15, 30, 26, 55, 32, 58] and part relations [6, 10, 31, 35, 60, 50] for individual 3D objects.”