Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations-Reference-Cited by-同舟云学术

Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations

Published:2022-06 Issue: Volume: Page:
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Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Sajjadi Mehdi S.M.¹,Meyer Henning¹,Pot Etienne¹,Bergmann Urs¹,Greff Klaus¹,Radwan Noha¹,Vora Suhani¹,Lucic Mario¹,Duckworth Daniel¹,Dosovitskiy Alexey¹,Uszkoreit Jakob¹,Funkhouser Thomas¹,Tagliasacchi Andrea²

Affiliation:

1. Google Research

2. Simon Fraser University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09878781.pdf?arnumber=9878781

Reference52 articles.

1. State of the Art on Neural Rendering

2. Light field networks: Neural scene representations with single-evaluation rendering;sitzmann;NeurIPS,2021

3. EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis

4. Geometryfree view synthesis: Transformers and no 3D priors;rombach;ICCV,2021

5. Mesh-based 3D textured urban mapping

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