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Computer Science > Computer Vision and Pattern Recognition

arXiv:2210.04847 (cs)
[Submitted on 10 Oct 2022 (v1), last revised 10 May 2023 (this version, v3)]

Title:NerfAcc: A General NeRF Acceleration Toolbox

Authors:Ruilong Li, Matthew Tancik, Angjoo Kanazawa
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Abstract:We propose NerfAcc, a toolbox for efficient volumetric rendering of radiance fields. We build on the techniques proposed in Instant-NGP, and extend these techniques to not only support bounded static scenes, but also for dynamic scenes and unbounded scenes. NerfAcc comes with a user-friendly Python API, and is ready for plug-and-play acceleration of most NeRFs. Various examples are provided to show how to use this toolbox. Code can be found here: this https URL. Note this write-up matches with NerfAcc v0.3.5. For the latest features in NerfAcc, please check out our more recent write-up at arXiv:2305.04966
Comments: Webpage: this https URL Updated Write-up: arXiv:2305.04966
Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
Cite as: arXiv:2210.04847 [cs.CV]
  (or arXiv:2210.04847v3 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2210.04847

Submission history

From: Ruilong Li [view email]
[v1] Mon, 10 Oct 2022 17:03:23 UTC (14 KB)
[v2] Thu, 13 Oct 2022 05:41:45 UTC (14 KB)
[v3] Wed, 10 May 2023 05:31:59 UTC (14 KB)
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