Neural Radiance Field training can be accelerated through the use of
grid-based representations in NeRF's learned mapping from spatial coordinates
to colors and volumetric density. However, these grid-based approaches lack an
explicit understanding of scale and therefore often introduce aliasing, usually
in the form of jaggies or missing scene content. Anti-aliasing has previously
been addressed by mip-NeRF 360, which reasons about sub-volumes along a cone
rather than points along a ray, but this approach is not natively compatible
with current grid-based techniques. We show how ideas from rendering and signal
processing can be used to construct a technique that combines mip-NeRF 360 and
grid-based models such as Instant NGP to yield error rates that are 8% - 76%
lower than either prior technique, and that trains 22x faster than mip-NeRF
360.

使用渲染和信号处理中的思想，将 mip-NeRF 360 和基于网格的模型相结合，使误差比之前的技术低 8%-76%，并且训练速度比 mip-NeRF 360 快 22 倍。

Zip-NeRF: 基于格点的反锯齿神经辐射场

Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields

In many recent works, multi-layer perceptions (MLPs) have been shown to be
suitable for modeling complex spatially-varying functions including images and
3D scenes. Although the MLPs are able to represent complex scenes with
unprecedented quality and memory footprint, this expressive power of the MLPs,
however, comes at the cost of long training and inference times. On the other
hand, bilinear/trilinear interpolation on regular grid based representations
can give fast training and inference times, but cannot match the quality of
MLPs without requiring significant additional memory. Hence, in this work, we
investigate what is the smallest change to grid-based representations that
allows for retaining the high fidelity result of MLPs while enabling fast
reconstruction and rendering times. We introduce a surprisingly simple change
that achieves this task -- simply allowing a fixed non-linearity (ReLU) on
interpolated grid values. When combined with coarse to-fine optimization, we
show that such an approach becomes competitive with the state-of-the-art. We
report results on radiance fields, and occupancy fields, and compare against
multiple existing alternatives. Code and data for the paper are available at
this https URL

通过对栅格化表示的最小更改，引入固定非线性 (ReLU)，结合更粗到更细的优化，快速重建、渲染，并在辐射场和占据场上进行比较，该方法在保留 MLP 高保真结果的同时实现了 MLPs 速度的变化。