Differentiable Neural Radiosity

We introduce Differentiable Neural Radiosity, a novel method of representing the solution of the differential rendering equation using a neural network. In- spired by neural radiosity techniques, we minimize the norm of the residual of the differential rendering equation to directly optimize our network. The network is capable of outputting continuous, view-independent gradients of the radiance field with respect to scene parameters, taking into account differential global illumination effects while keeping memory and time com- plexity constant in path length. To solve inverse rendering problems, we use a pre-trained instance of our network that represents the differential radiance field with respect to a limited number of scene parameters. In our experiments, we leverage this to achieve faster and more accurate con- vergence compared to other techniques such as Automatic Differentiation, Radiative Backpropagation, and Path Replay Backpropagation.

Description

  • Saeed Hadadan, Matthias Zwicker

  • arXiv

Technology