High-Fidelity and Real-Time Novel View Synthesis for Dynamic Scenes-Reference-Cited by-同舟云学术

High-Fidelity and Real-Time Novel View Synthesis for Dynamic Scenes

Published:2023-12-10 Issue: Volume: Page:
ISSN:
Container-title:SIGGRAPH Asia 2023 Conference Papers
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Author:

Lin Haotong¹^ORCID,Peng Sida²^ORCID,Xu Zhen¹^ORCID,Xie Tao¹^ORCID,He Xingyi¹^ORCID,Bao Hujun¹^ORCID,Zhou Xiaowei¹^ORCID

Affiliation:

1. State Key Laboratory of CAD & CG, Zhejiang University, China

2. Zhejiang University, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3610548.3618142

Reference95 articles.

1. Benjamin Attal, Jia-Bin Huang, Christian Richardt, Michael Zollhoefer, Johannes Kopf, Matthew O’Toole, and Changil Kim. 2023. HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sampling. arXiv preprint arXiv:2301.02238 (2023).

2. Learning Neural Light Fields with Ray-Space Embedding

3. Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. 2021. Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields. arXiv (2021).

4. Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields

5. Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. 2023. Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields. arXiv preprint arXiv:2304.06706 (2023).

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