DyLiN: Making Light Field Networks Dynamic
Heng Yu, Joel Julin, Zoltan A. Milacski, Koichiro Niinuma, László A. Jeni
Abstract
Render time: 3 s TiNeuVox [8] Render time: 7 s Ours Render time: 0.1 s Figure 1. Our proposed DyLiN for dynamic 3D scene rendering achieves higher quality than its HyperNeRF teacher model and the stateof-the-art TiNeuVox model, while being an order of magnitude faster. Right: DyLiN is of moderate storage size (shown by dot radii). For each method, the relative improvement in Peak Signal-to-Noise Ratio over NeRF (∆PSNR) is measured for the best-performing scene.
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Install the CLIlune papers fulltext 6c5fe982-8c6c-491f-b464-a84365948dbdCited by top-tier papers8
- CoGS: Controllable Gaussian SplattingHeng Yu, Joel Julin, Zoltán Ádám Milacski, Koichiro Niinuma et al.CVPR 2024 · 27 citations
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Builds on20
- Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman et al.ICCV 2021 · 2,700 citations
- NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view ReconstructionPeng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt et al.NeurIPS 2021 · 2,500 citations
- Neural Sparse Voxel FieldsLingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua et al.NeurIPS 2020 · 1,535 citations
- Nerfies: Deformable Neural Radiance FieldsKeunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz et al.ICCV 2021 · 1,442 citations
- PlenOctrees for Real-time Rendering of Neural Radiance FieldsAlex Yu, Ruilong Li, Matthew Tancik, Hao Li et al.ICCV 2021 · 1,284 citations
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