  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.
