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：在虚拟环境中提供全局光照的实时渲染对于赋予用户真实体验至关重要。我们提出了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相结合，以合成全局光照的高动态范围（HDR）结果。我们的方法解决了在使用神经网络时捕获长距离/远距离间接光照的挑战，并推广到处理复杂光照和场景。从求解器的神经网络思维到渲染方程，我们提出了一种新颖的网络架构来预测间接光照。我们的网络配备了一种改进的注意力机制，该机制在空间几何特征的引导下聚合全局信息，以及一种对每个颜色通道进行单独编码的单色设计。我们进行了广泛的评估，实验结果表明我们的方法优于先前基于学习的技术。我们的方法在处理复杂光照（如变色光照和环境光照）方面表现出色。它能够成功捕获远处的间接光照，并很好地模拟纹理表面之间的相互反射（即色彩溢出效应）；它还能有效处理训练数据集中未出现过的新场景。