This could be used that way, and there is some precedent, e.g. in RenderGAN (https://arxiv.org/abs/1611.01331 https://arxiv.org/abs/1611.01331). Note the GAN pa

This could be used that way, and there is some precedent, e.g. in RenderGAN (https://arxiv.org/abs/1611.01331 https://arxiv.org/abs/1611.01331). Note the GAN part: that's why the "differentiable" part is important, so you can integrate rendering into a trainable network. Otherwise, you don't really need the "differentiable" part, and in fact using synthetic renderings from traditional, established renderers (both rasterizing and raytracing) has been a pretty active area for a while now. There's also some approaches like SUGAN (https://machinelearning.apple.com/2017/07/07/GAN.html https://machinelearning.apple.com/2017/07/07/GAN.html), but those don't really use differentiable rendering -- they just use traditional rendering, then apply a 2D GAN on top to make them look more realistic (from a CNN's point of view, at least).