Abstract Rendering realistic images with global illumination is a computationally demanding task and often requires dedicated hardware for feasible runtime. Recent research uses Deep Neural Networks to predict indirect lighting on image level, but such methods are commonly limited to diffuse materials and require training on each scene. We present Deep Radiance Caching (DRC), an efficient variant of Radiance Caching utilizing Convolutional Autoencoders for rendering global illumination. DRC employs a denoising neural network with Radiance Caching to support a wide range of material types, without the requirement of offline pre-computation or training for each scene. This offers high performance CPU rendering for maximum accessibility. Our method has been evaluated on interior scenes, and is able to produce high-quality images within 180 s on a single CPU.

中文翻译：

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Deep Radiance Caching：更深入光线追踪的卷积自编码器

摘要 使用全局照明渲染逼真的图像是一项计算要求高的任务，通常需要专用硬件来实现可行的运行时间。最近的研究使用深度神经网络来预测图像级别的间接照明，但这些方法通常仅限于漫反射材料，并且需要对每个场景进行训练。我们提出了 Deep Radiance Caching (DRC)，它是 Radiance Caching 的一种有效变体，它利用卷积自编码器来渲染全局照明。DRC 使用带有 Radiance Caching 的去噪神经网络来支持广泛的材料类型，无需对每个场景进行离线预计算或训练。这提供了高性能 CPU 渲染以实现最大的可访问性。我们的方法已经在室内场景中进行了评估，