Real-time rendering with global illumination is crucial to afford the user realistic experience in virtual environments. We present a learning-based estimator to predict diffuse indirect illumination in screen space, which then is combined with direct illumination to synthesize globally-illuminated high dynamic range (HDR) results. Our approach tackles the challenges of capturing long-range/long-distance indirect illumination when employing neural networks and is generalized to handle complex lighting and scenarios. From the neural network thinking of the solver to the rendering equation, we present a novel network architecture to predict indirect illumination. Our network is equipped with a modified attention mechanism that aggregates global information guided by spacial geometry features, as well as a monochromatic design that encodes each color channel individually. We conducted extensive evaluations, and the experimental results demonstrate our superiority over previous learning-based techniques. Our approach excels at handling complex lighting such as varying-colored lighting and environment lighting. It can successfully capture distant indirect illumination and simulates the interreflections between textured surfaces well (i.e., color bleeding effects); it can also effectively handle new scenes that are not present in the training dataset.

翻译：具备全局光照的实时渲染对于在虚拟环境中为用户提供真实体验至关重要。本文提出一种基于学习的估计器，用于在屏幕空间预测漫反射间接光照，随后将其与直接光照结合以合成全局照明的高动态范围（HDR）结果。我们的方法解决了使用神经网络时捕捉长距离间接光照的挑战，并能泛化处理复杂光照与场景。从求解器的神经网络思维到渲染方程，我们提出一种新颖的网络架构来预测间接光照。该网络配备改进的注意力机制，通过空间几何特征引导聚合全局信息，并采用单色设计独立编码各颜色通道。我们进行了广泛评估，实验结果表明本方法优于以往基于学习的技术。我们的方法在处理复杂光照（如多色光源与环境光照）方面表现优异，能成功捕捉远距离间接光照并良好模拟纹理表面间的相互反射（即色彩渗溢效应）；同时能有效处理训练数据集中未出现过的新场景。