Physically correct, noise-free global illumination is crucial in physically-based rendering, but often takes a long time to compute. Recent approaches have exploited sparse sampling and filtering to accelerate this process but still cannot achieve interactive performance. It is partly due to the time-consuming ray sampling even at 1 sample per pixel, and partly because of the complexity of deep neural networks. To address this problem, we propose a novel method to generate plausible single-bounce indirect illumination for dynamic scenes in interactive framerates. In our method, we first compute direct illumination and then use a lightweight neural network to predict screen space indirect illumination. Our neural network is designed explicitly with bilateral convolution layers and takes only essential information as input (direct illumination, surface normals, and 3D positions). Also, our network maintains the coherence between adjacent image frames efficiently without heavy recurrent connections. Compared to state-of-the-art works, our method produces single-bounce indirect illumination of dynamic scenes with higher quality and better temporal coherence and runs at interactive framerates.

中文翻译：

---

用于交互式单次反射漫反射间接照明的轻量级双边卷积神经网络

物理上正确、无噪声的全局照明在基于物理的渲染中至关重要，但通常需要很长时间来计算。最近的方法利用稀疏采样和过滤来加速这个过程，但仍然无法实现交互性能。部分原因是即使每个像素只有 1 个样本，光线采样也很耗时，部分原因是深度神经网络的复杂性。为了解决这个问题，我们提出了一种新的方法来为交互式帧速率中的动态场景生成合理的单次反射间接照明。在我们的方法中，我们首先计算直接照明，然后使用轻量级神经网络来预测屏幕空间间接照明。我们的神经网络明确设计了双边卷积层，并且仅将基本信息作为输入（直接照明，表面法线和 3D 位置）。此外，我们的网络有效地保持了相邻图像帧之间的连贯性，而无需大量的循环连接。与最先进的作品相比，我们的方法产生了具有更高质量和更好时间相干性的动态场景的单次反射间接照明，并以交互式帧速率运行。