  Modern Neural Radiance Fields (NeRFs) learn a mapping from position to
volumetric density leveraging proposal network samplers. In contrast to the
coarse-to-fine sampling approach with two NeRFs, this offers significant
potential for acceleration using lower network capacity. Given that NeRFs
utilize most of their network capacity to estimate radiance, they could store
valuable density information in their parameters or their deep features. To
investigate this proposition, we take one step back and analyze large, trained
ReLU-MLPs used in coarse-to-fine sampling. Building on our novel activation
visualization method, we find that trained NeRFs, Mip-NeRFs and proposal
network samplers map samples with high density to local minima along a ray in
activation feature space. We show how these large MLPs can be accelerated by
transforming intermediate activations to a weight estimate, without any
modifications to the training protocol or the network architecture. With our
approach, we can reduce the computational requirements of trained NeRFs by up
to 50% with only a slight hit in rendering quality. Extensive experimental
evaluation on a variety of datasets and architectures demonstrates the
effectiveness of our approach. Consequently, our methodology provides valuable
insight into the inner workings of NeRFs.
