Inverse Global Illumination using a Neural Radiometric Prior
Inverse Global Illumination using a Neural Radiometric Prior
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DOI：
10.1145/3588432.3591553
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发表时间：
2023-05
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影响因子：
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通讯作者：
Saeed Hadadan;Geng Lin;Jan Novák;Fabrice Rousselle;Matthias Zwicker
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作者：
Saeed Hadadan;Geng Lin;Jan Novák;Fabrice Rousselle;Matthias Zwicker
考虑全局照明的逆渲染方法正变得越来越流行，但当前的方法需要通过跟踪多个光反弹来评估和自动区分数百万个路径积分，这仍然是昂贵的并且容易产生噪声。相反，本文提出了一个辐射先验作为一个简单的替代建立完整的路径积分在传统的可微路径跟踪器，同时仍然正确地占全球照明。受神经辐射度技术的启发，我们使用神经网络作为辐射度函数，并在逆渲染损失中引入由渲染方程的残差范数组成的先验。我们训练我们的辐射网络，并同时使用由渲染和多视图输入图像之间的光度项以及辐射先验（残差项）组成的损失来优化场景参数。该残差项对优化实施物理约束，以确保辐射场考虑全局照明。我们将我们的方法与香草可微路径跟踪器和更高级的技术（如路径重放反向传播）进行比较。尽管我们的方法简单，我们可以恢复场景参数具有可比性，在某些情况下更好的质量，在相当低的计算时间。
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.