  Inverse rendering methods that account for global illumination are becoming
more popular, but current methods require evaluating and automatically
differentiating millions of path integrals by tracing multiple light bounces,
which remains expensive and prone to noise. Instead, this paper proposes a
radiometric prior as a simple alternative to building complete path integrals
in a traditional differentiable path tracer, while still correctly accounting
for global illumination. Inspired by the Neural Radiosity technique, we use a
neural network as a radiance function, and we introduce a prior consisting of
the norm of the residual of the rendering equation in the inverse rendering
loss. We train our radiance network and optimize scene parameters
simultaneously using a loss consisting of both a photometric term between
renderings and the multi-view input images, and our radiometric prior (the
residual term). This residual term enforces a physical constraint on the
optimization that ensures that the radiance field accounts for global
illumination. We compare our method to a vanilla differentiable path tracer,
and more advanced techniques such as Path Replay Backpropagation. Despite the
simplicity of our approach, we can recover scene parameters with comparable and
in some cases better quality, at considerably lower computation times.
