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.

翻译：我们引入了差异神经无线电, 这是一种新颖的方法, 通过神经网络代表差异转换方程式的解决方案。 在神经放射技术的启发下, 我们最大限度地减少差异转换方程式剩余值的规范, 以便直接优化我们的网络 。 这个网络能够输出连续的、 视向独立的光场梯度, 相对于场景参数, 同时考虑到不同的全球光化效应, 同时在路径长度上保持记忆和时间复杂性的常数 。 为了解决反向转换问题, 我们使用我们网络的预培训实例, 代表有限的场景参数的差弧场 。 在我们的实验中, 我们利用它来实现更快和更精确的趋同, 与其他技术相比, 如自动差异、 辐射反光反光和路径反向调整等 。