  DIVeR builds on the key ideas of NeRF and its variants -- density models and
volume rendering -- to learn 3D object models that can be rendered
realistically from small numbers of images. In contrast to all previous NeRF
methods, DIVeR uses deterministic rather than stochastic estimates of the
volume rendering integral. DIVeR's representation is a voxel based field of
features. To compute the volume rendering integral, a ray is broken into
intervals, one per voxel; components of the volume rendering integral are
estimated from the features for each interval using an MLP, and the components
are aggregated. As a result, DIVeR can render thin translucent structures that
are missed by other integrators. Furthermore, DIVeR's representation has
semantics that is relatively exposed compared to other such methods -- moving
feature vectors around in the voxel space results in natural edits. Extensive
qualitative and quantitative comparisons to current state-of-the-art methods
show that DIVeR produces models that (1) render at or above state-of-the-art
quality, (2) are very small without being baked, (3) render very fast without
being baked, and (4) can be edited in natural ways.
