We introduce inverse transport networks as a learning architecture for inverse rendering problems where, given input image measurements, we seek to infer physical scene parameters such as shape, material, and illumination. During training, these networks are evaluated not only in terms of how close they can predict groundtruth parameters, but also in terms of whether the parameters they produce can be used, together with physically-accurate graphics renderers, to reproduce the input image measurements. To en- able training of inverse transport networks using stochastic gradient descent, we additionally create a general-purpose, physically-accurate differentiable renderer, which can be used to estimate derivatives of images with respect to arbitrary physical scene parameters. Our experiments demonstrate that inverse transport networks can be trained efficiently using differentiable rendering, and that they generalize to scenes with completely unseen geometry and illumination better than networks trained without appearance- matching regularization.

本研究提出了逆向传输网络作为学习体系结构，以解决逆渲染问题，其中我们通过给定输入图像测量值来推断物理场景参数，如形状、材料和光照。我们还创建了一个通用的物理精确可微分渲染器，并证明了逆向传输网络可以有效地进行训练，并且在出现从未见过的场景时有更好的泛化性能。

反向传输网络