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DIVeR builds on the key ideas of NeRF and its variants -- density models andvolume rendering -- to learn 3D object models that can be renderedrealistically from small numbers of images. In contrast to all previous NeRFmethods, DIVeR uses deterministic rather than stochastic estimates of thevolume rendering integral. DIVeR's representation is a voxel based field offeatures. To compute the volume rendering integral, a ray is broken intointervals, one per voxel; components of the volume rendering integral areestimated from the features for each interval using an MLP, and the componentsare aggregated. As a result, DIVeR can render thin translucent structures thatare missed by other integrators. Furthermore, DIVeR's representation hassemantics that is relatively exposed compared to other such methods -- movingfeature vectors around in the voxel space results in natural edits. Extensivequalitative and quantitative comparisons to current state-of-the-art methodsshow that DIVeR produces models that (1) render at or above state-of-the-artquality, (2) are very small without being baked, (3) render very fast withoutbeing baked, and (4) can be edited in natural ways.

DIVeR: Real-time and Accurate Neural Radiance Fields with Deterministic Integration for Volume Rendering