D-NeRF: Neural Radiance Fields for Dynamic Scenes
Albert Pumarola, Enric Corona, Gerard Pons-Moll, Francesc Moreno-Noguer
2021年份
539顶会引用
摘要
Point of View & Time Figure 1: We propose D-NeRF, a method for synthesizing novel views, at an arbitrary point in time, of dynamic scenes with complex non-rigid geometries. We optimize an underlying deformable volumetric function from a sparse set of input monocular views without the need of ground-truth geometry nor multi-view images. The figure shows two scenes under variable points of view and time instances synthesised by the proposed model.
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引用它的顶会 Paper539
- MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View StereoAnpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang 等ICCV 2021 · 被引用 1,024 次
- Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields ReconstructionCheng Sun, Min Sun, Hwann-Tzong ChenCVPR 2022 · 被引用 859 次
- Depth-supervised NeRF: Fewer Views and Faster Training for FreeKangle Deng, Andrew Liu, Jun-Yan Zhu, Deva RamananCVPR 2022 · 被引用 756 次
- Nerfstudio: A Modular Framework for Neural Radiance Field DevelopmentMatthew Tancik, Ethan Weber, Evonne Ng, Ruilong Li 等SIGGRAPH 2023 · 被引用 592 次
- Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian SplattingZeyu Yang, Hongye Yang, Zijie Pan, Li ZhangICLR 2024 · 被引用 529 次
它引用的顶会 Paper14
- Neural Sparse Voxel FieldsLingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua 等NeurIPS 2020 · 被引用 1,535 次
- Multiview Neural Surface Reconstruction by Disentangling Geometry and AppearanceLior Yariv, Yoni Kasten, Dror Moran, Meirav Galun 等NeurIPS 2020 · 被引用 1,010 次
- Neural Unsigned Distance Fields for Implicit Function LearningJulian Chibane, Aymen Mir, Gerard Pons-MollNeurIPS 2020 · 被引用 415 次
- Occupancy Flow: 4D Reconstruction by Learning Particle DynamicsMichael Niemeyer, Lars M. Mescheder, Michael Oechsle, Andreas GeigerICCV 2019 · 被引用 314 次
- Extreme View SynthesisInchang Choi, Orazio Gallo, Alejandro J. Troccoli, Min H. Kim 等ICCV 2019 · 被引用 207 次
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