Neural BSSRDF: Object Appearance Representation Including Heterogeneous Subsurface Scattering

Abstract

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.

References in corpus (7)

• Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
• NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination
• Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks
• Neural Light Transport for Relighting and View Synthesis
• Relighting Neural Radiance Fields with Shadow and Highlight Hints
• A Data-Driven Paradigm for Precomputed Radiance Transfer
• Fast Image-based Neural Relighting with Translucency-Reflection Modeling