Neural volumetric representations such as Neural Radiance Fields (NeRF) have
emerged as a compelling technique for learning to represent 3D scenes from
images with the goal of rendering photorealistic images of the scene from
unobserved viewpoints. However, NeRF's computational requirements are
prohibitive for real-time applications: rendering views from a trained NeRF
requires querying a multilayer perceptron (MLP) hundreds of times per ray. We
present a method to train a NeRF, then precompute and store (i.e. "bake") it as
a novel representation called a Sparse Neural Radiance Grid (SNeRG) that
enables real-time rendering on commodity hardware. To achieve this, we
introduce 1) a reformulation of NeRF's architecture, and 2) a sparse voxel grid
representation with learned feature vectors. The resulting scene representation
retains NeRF's ability to render fine geometric details and view-dependent
appearance, is compact (averaging less than 90 MB per scene), and can be
rendered in real-time (higher than 30 frames per second on a laptop GPU).
Actual screen captures are shown in our video.

该论文提出了一种名为 Sparse Neural Radiance Grid 的新方法，它使用学习的稀疏体素网格表示，通过对神经辐射场（Neural Radiance Fields）进行预处理和存储（烘焙）来实现实时渲染。通过该方法，可以在以往的基础上保留了 NeRF 渲染精细几何细节和视图相关外观的能力，实现在普通硬件上进行实时渲染。