  We introduce Differentiable Neural Radiosity, a novel method of representing
the solution of the differential rendering equation using a neural network.
Inspired 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
complexity 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 convergence compared to
other techniques such as Automatic Differentiation, Radiative Backpropagation,
and Path Replay Backpropagation.
