NerfAcc: A General NeRF Acceleration Toolbox
Ruilong Li, Matthew Tancik, Angjoo Kanazawa
TL;DR
NerfAcc is a versatile toolbox that accelerates volumetric rendering of radiance fields, supporting static, dynamic, and unbounded scenes with a user-friendly API, enhancing NeRF performance and usability.
Contribution
It extends Instant-NGP techniques to support various scene types and provides a plug-and-play Python API for efficient NeRF rendering.
Findings
Supports static, dynamic, and unbounded scenes
Provides significant acceleration in volumetric rendering
Offers a user-friendly Python API
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: https://github.com/KAIR-BAIR/nerfacc. 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
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · Advanced Vision and Imaging · Image Enhancement Techniques