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：考虑全局光照的逆向渲染方法日益流行，但现有方法需要追踪多次光线反弹来评估和自动微分数百万条路径积分，这仍然计算成本高昂且易受噪声影响。本文提出了一种辐射度先验，作为传统可微分路径追踪器中构建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，我们使用神经网络作为辐射度函数，并在逆向渲染损失中引入由渲染方程残差范数构成的先验。我们通过联合优化包含渲染结果与多视角输入图像光度项和辐射度先验（残差项）的损失函数，同步训练辐射度网络并优化场景参数。该残差项为优化施加了物理约束，确保辐射场能够反映全局光照。我们将本方法与原始可微分路径追踪器及Path Replay Backpropagation等先进技术进行了比较。尽管方法简单，我们仍能以显著降低的计算时间获得相当甚至更优的场景参数重建质量。