Neural Radiosity

TL;DR

Neural Radiosity introduces a neural network-based method to solve the rendering equation, enabling efficient scene rendering with non-diffuse surfaces by minimizing residuals in radiance distribution.

Contribution

It leverages neural networks to represent full radiance distributions, surpassing traditional basis functions limited to diffuse surfaces, and decouples scene solving from image rendering.

Findings

Effective on scenes with non-diffuse surfaces

Uses geometric learnable features for better convergence

Allows arbitrary view synthesis of scenes

Abstract

We introduce Neural Radiosity, an algorithm to solve the rendering equation by minimizing the norm of its residual similar as in traditional radiosity techniques. Traditional basis functions used in radiosity techniques, such as piecewise polynomials or meshless basis functions are typically limited to representing isotropic scattering from diffuse surfaces. Instead, we propose to leverage neural networks to represent the full four-dimensional radiance distribution, directly optimizing network parameters to minimize the norm of the residual. Our approach decouples solving the rendering equation from rendering (perspective) images similar as in traditional radiosity techniques, and allows us to efficiently synthesize arbitrary views of a scene. In addition, we propose a network architecture using geometric learnable features that improves convergence of our solver compared to previous techniques. Our approach leads to an algorithm that is simple to implement, and we demonstrate its effectiveness on a variety of scenes with non-diffuse surfaces.