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231224s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2017.2689007
|2 doi
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|a eng
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| 100 |
1 |
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|a Liu, Xiaobai
|e verfasserin
|4 aut
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| 245 |
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|a Single-View 3D Scene Reconstruction and Parsing by Attribute Grammar
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| 264 |
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|c 2018
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|a Text
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 13.02.2019
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|a Date Revised 15.02.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a In this paper, we present an attribute grammar for solving two coupled tasks: i) parsing a 2D image into semantic regions; and ii) recovering the 3D scene structures of all regions. The proposed grammar consists of a set of production rules, each describing a kind of spatial relation between planar surfaces in 3D scenes. These production rules are used to decompose an input image into a hierarchical parse graph representation where each graph node indicates a planar surface or a composite surface. Different from other stochastic image grammars, the proposed grammar augments each graph node with a set of attribute variables to depict scene-level global geometry, e.g., camera focal length, or local geometry, e.g., surface normal, contact lines between surfaces. These geometric attributes impose constraints between a node and its off-springs in the parse graph. Under a probabilistic framework, we develop a Markov Chain Monte Carlo method to construct a parse graph that optimizes the 2D image recognition and 3D scene reconstruction purposes simultaneously. We evaluated our method on both public benchmarks and newly collected datasets. Experiments demonstrate that the proposed method is capable of achieving state-of-the-art scene reconstruction of a single image
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|a Journal Article
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| 650 |
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
1 |
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|a Zhao, Yibiao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhu, Song-Chun
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 40(2018), 3 vom: 03. März, Seite 710-725
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|x 1939-3539
|7 nnns
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|g volume:40
|g year:2018
|g number:3
|g day:03
|g month:03
|g pages:710-725
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|u http://dx.doi.org/10.1109/TPAMI.2017.2689007
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