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Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields

2023-04-13 · ICCV 2023 1 · Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, Peter Hedman

Neural Radiance Field training can be accelerated through the use of grid-based representations in NeRF's learned mapping from spatial coordinates to colors and volumetric density. However, these grid-based approaches lack an explicit understanding of scale and therefore often introduce aliasing, usually in the form of jaggies or missing scene content. Anti-aliasing has previously been addressed by mip-NeRF 360, which reasons about sub-volumes along a cone rather than points along a ray, but this approach is not natively compatible with current grid-based techniques. We show how ideas from rendering and signal processing can be used to construct a technique that combines mip-NeRF 360 and grid-based models such as Instant NGP to yield error rates that are 8% - 77% lower than either prior technique, and that trains 24x faster than mip-NeRF 360.

📄 PDF Abstract BibTeX arXiv:2304.06706

Code (1)

SuLvXiangXin/zipnerf-pytorch pytorch

Tasks

NeRFNovel View SynthesisSpeech Enhancement

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