Data‐Driven Shape Analysis and Processing

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dc.contributor.authorXu, Kaien_US
dc.contributor.authorKim, Vladimir G.en_US
dc.contributor.authorHuang, Qixingen_US
dc.contributor.authorKalogerakis, Evangelosen_US
dc.contributor.editorChen, Min and Zhang, Hao (Richard)en_US
dc.date.accessioned2017-03-13T18:13:01Z
dc.date.available2017-03-13T18:13:01Z
dc.date.issued2017
dc.description.abstractData‐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, modelling and editing of shapes. Data‐driven methods are also able to learn computational models that reason about properties and relationships of shapes without relying on hard‐coded rules or explicitly programmed instructions. 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, modelling 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.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, modelling and editing of shapes. Data‐driven methods are also able to learn computational models that reason about properties and relationships of shapes without relying on hard‐coded rules or explicitly programmed instructions. 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, modelling 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.en_US
dc.description.documenttypestar
dc.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume36
dc.identifier.doi10.1111/cgf.12790
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.12790
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf12790
dc.publisher© 2017 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectShape analysis
dc.subjectshape processing
dc.subjectshape modeling
dc.subjectdata‐driven approach
dc.subjectmachine learning
dc.subjectI.3.5 [Computer Graphics]: Computational Geometry and Object Modeling
dc.titleData‐Driven Shape Analysis and Processingen_US
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