Local Implicit Ray Function for Generalizable Radiance Field Representation
Xin Huang, Qi Zhang, Ying Feng, Xiaoyu Li, Xuan Wang, Qing Wang
摘要
Figure 1 . We propose LIRF to reconstruct radiance fields of unseen scenes for novel view synthesis. Given that current generalizable NeRF-like methods cast an infinitesimal ray to render a pixel at different scales, it causes excessive blurring and aliasing. Our method instead reasons about 3D conical frustums defined by the neighbor rays through the neighbor pixels (as shown in (a)). Our LIRF outputs the feature of any sample within the conical frustum in a continuous manner (as shown in (b)), which supports NeRF reconstruction at arbitrary scales. Compared with the previous method, our method can be generalized to represent the same unseen scene at multiple levels of details (as shown in (c)). Specifically, given a set of input views at a consistent image scale ×1, LIRF enables our method to both preserve sharp details in close-up shots (anti-blurring as shown in ×2 and ×4 results) and correctly render the zoomed-out images (anti-aliasing as shown in ×0.5 results).
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引用它的顶会 Paper10
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它引用的顶会 Paper28
- 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
- Volume Rendering of Neural Implicit SurfacesLior Yariv, Jiatao Gu, Yoni Kasten, Yaron LipmanNeurIPS 2021 · 被引用 1,421 次
- Plenoxels: Radiance Fields without Neural NetworksSara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen 等CVPR 2022 · 被引用 1,237 次
- 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
- NeRD: Neural Reflectance Decomposition from Image CollectionsMark Boss, Raphael Braun, Varun Jampani, Jonathan T. Barron 等ICCV 2021 · 被引用 608 次
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