Data-driven shape analysis and processing

Kai Xu, Vladimir G. Kim, Qixing Huang, Niloy Mitra, Evangelos Kalogerakis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

52 Scopus citations


Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.

Original languageEnglish (US)
Title of host publicationSA 2016 - SIGGRAPH ASIA 2016 Courses
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450345385
StatePublished - Nov 28 2016
Event2016 SIGGRAPH ASIA Courses, SA 2016 - Macau, China
Duration: Dec 5 2016Dec 8 2016

Publication series

NameSA 2016 - SIGGRAPH ASIA 2016 Courses


Other2016 SIGGRAPH ASIA Courses, SA 2016

Bibliographical note

Funding Information:
We thank Zimo Li for proofreading this survey and the anonymous reviewers for helpful suggestions. Kalogerakis gratefully acknowledges support from NSF (CHS-1422441). Kai Xu is supported by NSFC (61572507, 61202333 and 61532003).


  • Geometry analysis
  • Geometry processing
  • Machine learning

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition


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