  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.

Computer Vision and Pattern Recognition;Graphics