NerfAcc:高效采样加速神经辐射场
NerfAcc: Efficient Sampling Accelerates NeRFs
May 8, 2023
作者: Ruilong Li, Hang Gao, Matthew Tancik, Angjoo Kanazawa
cs.AI
摘要
由于体积渲染所需的大量样本,优化和渲染神经辐射场具有极高的计算成本。最近的研究已经包括了替代采样方法,以帮助加速他们的方法,然而,它们通常不是工作的重点。在本文中,我们调查并比较多种采样方法,并展示改进的采样通常适用于统一的透射率估计概念下的各种NeRF变体。为了促进未来的实验,我们开发了NerfAcc,一个Python工具包,提供灵活的API,用于将先进的采样方法纳入与NeRF相关的方法中。我们展示了它的灵活性,通过展示它可以将几种最近的NeRF方法的训练时间缩短1.5倍至20倍,而对现有代码库的修改很小。此外,高度定制的NeRF,如Instant-NGP,可以使用NerfAcc在原生PyTorch中实现。
English
Optimizing and rendering Neural Radiance Fields is computationally expensive
due to the vast number of samples required by volume rendering. Recent works
have included alternative sampling approaches to help accelerate their methods,
however, they are often not the focus of the work. In this paper, we
investigate and compare multiple sampling approaches and demonstrate that
improved sampling is generally applicable across NeRF variants under an unified
concept of transmittance estimator. To facilitate future experiments, we
develop NerfAcc, a Python toolbox that provides flexible APIs for incorporating
advanced sampling methods into NeRF related methods. We demonstrate its
flexibility by showing that it can reduce the training time of several recent
NeRF methods by 1.5x to 20x with minimal modifications to the existing
codebase. Additionally, highly customized NeRFs, such as Instant-NGP, can be
implemented in native PyTorch using NerfAcc.