  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.
