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.

翻译：能够考虑全局光照的逆渲染方法正日益流行，但当前方法需要追踪多条光线反弹来求值和自动微分数百万个路径积分，这依然成本高昂且易受噪声影响。为此，本文提出一种辐射先验作为构建传统可微路径追踪器中完整路径积分的简单替代方案，同时仍能正确考虑全局光照。受神经辐射度技术启发，我们采用神经网络作为辐射函数，并引入一个由渲染方程残差范数构成的先验项纳入逆渲染损失。我们通过联合优化辐射网络与场景参数，其损失函数由渲染结果与多视角输入图像之间的光度项以及辐射先验（残差项）共同组成。该残差项对优化施加物理约束，确保辐射场能够反映全局光照效果。我们将本方法与标准可微路径追踪器及Path Replay Backpropagation等先进技术进行对比。尽管方法简洁，但我们能以显著更低的计算代价恢复场景参数，且质量相当甚至在某些情况下更优。