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Optimizing and rendering Neural Radiance Fields is computationally expensive due to the vast number of samples required by volume rendering.
13. various techniques used in connection with random digits
John Von Neumann · 1951
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Production volume rendering: Siggraph 2017 course
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Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari · 2020
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Nerf++: Analyzing and improving neural radiance fields
Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun · 2020
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Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields
Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan · 2021
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Barf: Bundle-adjusting neural radiance fields
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2021
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Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction
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Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields
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Neural geometric level of detail: Real-time rendering with implicit 3d shapes
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Volume rendering of neural implicit surfaces
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Instant neural graphics primitives with a multiresolution hash encoding
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Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction
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Improved direct voxel grid optimization for radiance fields reconstruction
Cheng Sun, Min Sun, and Hwann-Tzong Chen · 2022
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Fast dynamic radiance fields with time-aware neural voxels
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Variable bitrate neural fields
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Zip-nerf: Anti-aliased grid-based neural radiance fields
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