Temporal Interpolation is all You Need for Dynamic Neural Radiance Fields
Sungheon Park, Minjung Son, Seokhwan Jang, Young Chun Ahn, Ji-Yeon Kim, Nahyup Kang
2023年份
27顶会引用
摘要
Figure 1. We propose simple yet effective feature interpolation methods for training neural radiance fields of dynamic scenes based on temporal interpolation. We provide two different feature vector representations, neural representation (top) and grid representation (bottom), both of which are the concatenation of static feature vectors and temporally-interpolated dynamic feature vectors. The neural representation exhibits high-quality rendering performance with small-sized models while the grid representation shows competitive rendering results with astonishingly fast training speed.
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引用它的顶会 Paper27
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- Grid4D: 4D Decomposed Hash Encoding for High-Fidelity Dynamic Gaussian SplattingJiawei Xu, Zexin Fan, Jian Yang, Jin XieNeurIPS 2024 · 被引用 64 次
- Superpoint Gaussian Splatting for Real-Time High-Fidelity Dynamic Scene ReconstructionDiwen Wan, Ruijie Lu, Gang ZengICML 2024 · 被引用 43 次
- NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic VideosJinxi Li, Ziyang Song, Bo YangNeurIPS 2023 · 被引用 39 次
- Sync-NeRF: Generalizing Dynamic NeRFs to Unsynchronized VideosSeoha Kim, Jeongmin Bae, Youngsik Yun, Hahyun Lee 等AAAI 2024 · 被引用 19 次
它引用的顶会 Paper16
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- Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman 等ICCV 2021 · 被引用 2,700 次
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- Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields ReconstructionCheng Sun, Min Sun, Hwann-Tzong ChenCVPR 2022 · 被引用 859 次
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