  Monte Carlo rendering of translucent objects with heterogeneous scattering
properties is often expensive both in terms of memory and computation. If we do
path tracing and use a high dynamic range lighting environment, the rendering
becomes computationally heavy. We propose a compact and efficient neural method
for representing and rendering the appearance of heterogeneous translucent
objects. The neural representation function resembles a bidirectional
scattering-surface reflectance distribution function (BSSRDF). However,
conventional BSSRDF models assume a planar half-space medium and only surface
variation of the material, which is often not a good representation of the
appearance of real-world objects. Our method represents the BSSRDF of a full
object taking its geometry and heterogeneities into account. This is similar to
a neural radiance field, but our representation works for an arbitrary distant
lighting environment. In a sense, we present a version of neural precomputed
radiance transfer that captures all-frequency relighting of heterogeneous
translucent objects. We use a multi-layer perceptron (MLP) with skip
connections to represent the appearance of an object as a function of spatial
position, direction of observation, and direction of incidence. The latter is
considered a directional light incident across the entire non-self-shadowed
part of the object. We demonstrate the ability of our method to store highly
complex materials while having high accuracy when comparing to reference images
of the represented object in unseen lighting environments. As compared with
path tracing of a heterogeneous light scattering volume behind a refractive
interface, our method more easily enables importance sampling of the directions
of incidence and can be integrated into existing rendering frameworks while
achieving interactive frame rates.
