Abstract
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 language | English (US) |
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Title of host publication | SA 2016 - SIGGRAPH ASIA 2016 Courses |
Publisher | Association for Computing Machinery, Inc |
ISBN (Electronic) | 9781450345385 |
DOIs | |
State | Published - Nov 28 2016 |
Externally published | Yes |
Event | 2016 SIGGRAPH ASIA Courses, SA 2016 - Macau, China Duration: Dec 5 2016 → Dec 8 2016 |
Publication series
Name | SA 2016 - SIGGRAPH ASIA 2016 Courses |
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Other
Other | 2016 SIGGRAPH ASIA Courses, SA 2016 |
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Country/Territory | China |
City | Macau |
Period | 12/5/16 → 12/8/16 |
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).
Keywords
- Geometry analysis
- Geometry processing
- Machine learning
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition