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|a 10.1109/TPAMI.2019.2898859
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|a eng
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| 100 |
1 |
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|a Tulsiani, Shubham
|e verfasserin
|4 aut
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| 245 |
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|a Multi-View Supervision for Single-View Reconstruction via Differentiable Ray Consistency
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|c 2022
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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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|a Date Revised 08.11.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a We study the notion of consistency between a 3D shape and a 2D observation and propose a differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view. We do so by reformulating view consistency using a differentiable ray consistency (DRC) term. We show that this formulation can be incorporated in a learning framework to leverage different types of multi-view observations e.g., foreground masks, depth, color images, semantics etc. as supervision for learning single-view 3D prediction. We present empirical analysis of our technique in a controlled setting. We also show that this approach allows us to improve over existing techniques for single-view reconstruction of objects from the PASCAL VOC dataset
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| 650 |
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|a Journal Article
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| 700 |
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|a Zhou, Tinghui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Efros, Alexei A
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Malik, Jitendra
|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 44(2022), 12 vom: 14. Dez., Seite 8754-8765
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|x 1939-3539
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|g volume:44
|g year:2022
|g number:12
|g day:14
|g month:12
|g pages:8754-8765
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|u http://dx.doi.org/10.1109/TPAMI.2019.2898859
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