Stochastic progressive photon mapping (SPPM) is one of the important global illumination methods in computer graphics. It can simulate caustics and specular-diffuse-specular lighting effects efficiently. However, as a biased method, it always suffers from both bias and variance with limited iterations, and the bias and the variance bring multi-scale noises into SPPM renderings. Recent learning-based methods have shown great advantages on denoising unbiased Monte Carlo (MC) methods, but have not been leveraged for biased ones. In this paper, we present the first learning-based method specially designed for denoising-biased SPPM renderings. Firstly, to avoid conflicting denoising constraints, the radiance of final images is decomposed into two components: caustic and global. These two components are then denoised separately via a two-network framework. In each network, we employ a novel multi-residual block with two sizes of filters, which significantly improves the model’s capabilities, and makes it more suitable for multi-scale noises on both low-frequency and high-frequency areas. We also present a series of photon-related auxiliary features, to better handle noises while preserving illumination details, especially caustics. Compared with other state-of-the-art learning-based denoising methods that we apply to this problem, our method shows a higher denoising quality, which could efficiently denoise multi-scale noises while keeping sharp illuminations.

中文翻译：

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使用多残差网络去噪随机渐进光子映射渲染

随机渐进光子映射（SPPM）是计算机图形学中重要的全局照明方法之一。它可以有效地模拟焦散和镜面漫反射镜面照明效果。然而，作为一种有偏差的方法，它总是在迭代次数有限的情况下同时受到偏差和方差的影响，并且偏差和方差给 SPPM 渲染带来了多尺度噪声。最近的基于学习的方法在去噪无偏蒙特卡洛 (MC) 方法方面显示出巨大优势，但尚未用于有偏方法。在本文中，我们提出了第一个专门为去噪偏置 SPPM 渲染设计的基于学习的方法。首先，为了避免去噪约束冲突，最终图像的辐射被分解为两个分量：焦散和全局。然后通过双网络框架分别对这两个组件进行去噪。在每个网络中，我们采用了具有两种尺寸滤波器的新型多残差块，这显着提高了模型的能力，使其更适合低频和高频区域的多尺度噪声。我们还提供了一系列与光子相关的辅助特征，以更好地处理噪声，同时保留照明细节，尤其是焦散。与我们应用于此问题的其他最先进的基于学习的去噪方法相比，我们的方法显示出更高的去噪质量，可以在保持清晰照明的同时有效地对多尺度噪声进行去噪。使其更适合低频和高频区域的多尺度噪声。我们还提供了一系列与光子相关的辅助特征，以更好地处理噪声，同时保留照明细节，尤其是焦散。与我们应用于此问题的其他最先进的基于学习的去噪方法相比，我们的方法显示出更高的去噪质量，可以在保持清晰照明的同时有效地对多尺度噪声进行去噪。使其更适合低频和高频区域的多尺度噪声。我们还提供了一系列与光子相关的辅助特征，以更好地处理噪声，同时保留照明细节，尤其是焦散。与我们应用于此问题的其他最先进的基于学习的去噪方法相比，我们的方法显示出更高的去噪质量，可以在保持清晰照明的同时有效地对多尺度噪声进行去噪。