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Single-View View Synthesis With Multiplane Images

Tucker, Richard, Snavely, Noah·2020·2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr42600.2020.00063
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  • Single-image 3D view synthesis is particularly challenging, typically requiring the learning of a strong 3D prior model from multi-view image datasets.

    “Within this line of research, single-image 3D view synthesis (Charatan et al., 2024; Lin et al., 2023; Tucker and Snavely, 2020; Wiles et al., 2020; Yu et al., 2021) is particularly challenging, typically requiring the learning of a strong 3D prior model from multi-view image datasets.”

    From Cosmos World Foundation Model Platform for Physical AI (2025) · cites this paper as Tucker and Snavely, 2020

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Research works printed but not linked: 35

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  9. Nima Khademi Kalantari, Ting-Chun Wang, and Ravi Ra- mamoorthi. Learning-based view synthesis for light field cameras. ACM Trans. Graph., 35(6):193:1-193:10, 2016.
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  13. Marc Levoy and Pat Hanrahan. Light field rendering. In Proceedings of SIGGRAPH 96, Annual Conference Series, 1996.
  14. Zhengqi Li, Tali Dekel, Forrester Cole, Richard Tucker, Noah Snavely, Ce Liu, and William T. Freeman. Learning the depths of moving people by watching frozen people. In The IEEE Conference on Computer Vision and Pattern Recogni- tion (CVPR), June 2019.
  15. Zhengqi Li and Noah Snavely. Megadepth: Learning single- view depth prediction from internet photos. In The IEEE Con- ference on Computer Vision and Pattern Recognition (CVPR), June 2018.
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  19. Simon Niklaus, Long Mai, Jimei Yang, and Feng Liu. 3D Ken Burns effect from a single image. ACM Transactions on Graphics (TOG), 2019.
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Authors
Tucker, R., & Snavely, N.
Venue
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2020-06
Pages
548-557
DOI
10.1109/cvpr42600.2020.00063
Cited by
281
References
2
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