research-article

Compact Neural Graphics Primitives with Learned Hash Probing

Authors:

Towaki Takikawa,

Thomas Müller,

Merlin Nimier-David,

Alexander KellerAuthors Info & Claims

SA '23: SIGGRAPH Asia 2023 Conference Papers

Article No.: 120, Pages 1 - 10

https://doi.org/10.1145/3610548.3618167

Published: 11 December 2023 Publication History

Abstract

Neural graphics primitives are faster and achieve higher quality when their neural networks are augmented by spatial data structures that hold trainable features arranged in a grid. However, existing feature grids either come with a large memory footprint (dense or factorized grids, trees, and hash tables) or slow performance (index learning and vector quantization). In this paper, we show that a hash table with learned probes has neither disadvantage, resulting in a favorable combination of size and speed. Inference is faster than unprobed hash tables at equal quality while training is only 1.2–2.6 × slower, significantly outperforming prior index learning approaches. We arrive at this formulation by casting all feature grids into a common framework: they each correspond to a lookup function that indexes into a table of feature vectors. In this framework, the lookup functions of existing data structures can be combined by simple arithmetic combinations of their indices, resulting in Pareto optimal compression and speed.

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Index Terms

Compact Neural Graphics Primitives with Learned Hash Probing
1. Computing methodologies
  1. Computer graphics

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SA '23: SIGGRAPH Asia 2023 Conference Papers

December 2023

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ISBN:9798400703157

DOI:10.1145/3610548

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Shah IGamboa LGruson ANarayanan P(2024)Neural Histogram‐Based Glint Rendering of Surfaces With Spatially Varying RoughnessComputer Graphics Forum10.1111/cgf.1515743:4Online publication date: 24-Jul-2024
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Trevithick AChan MTakikawa TIqbal UDe Mello SChandraker MRamamoorthi RNagano K(2024)What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02148(22765-22775)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02148
Dou YZheng ZJin QNi BChen YKe J(2024)Real-Time Neural BRDF with Spherically Distributed Primitives2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00415(4337-4346)Online publication date: 16-Jun-2024
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Gordon CE. MacDonald LSaratchandran HLucey S(2024)D’OH: Decoder-Only Random Hypernetworks for Implicit Neural RepresentationsComputer Vision – ACCV 202410.1007/978-981-96-0963-5_8(128-147)Online publication date: 8-Dec-2024
https://doi.org/10.1007/978-981-96-0963-5_8
Govindarajan SSambugaro ZShabanov ATakikawa TRebain DSun WConci NYi KTagliasacchi A(2024)Lagrangian Hashing for Compressed Neural Field RepresentationsComputer Vision – ECCV 202410.1007/978-3-031-73383-3_11(183-199)Online publication date: 29-Sep-2024
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