Monte Carlo (MC) integration is used ubiquitously in realistic image synthesis because of its flexibility and generality. However, the integration has to balance estimator bias and variance, which causes visually distracting noise with low sample counts. Existing solutions fall into two categories, in-process sampling schemes and post-processing reconstruction schemes. This report summarizes recent trends in the post-processing reconstruction scheme. Recent years have seen increasing attention and significant progress in denoising MC rendering with deep learning, by training neural networks to reconstruct denoised rendering results from sparse MC samples. Many of these techniques show promising results in real-world applications, and this report aims to provide an assessment of these approaches for practitioners and researchers.

蒙特卡洛（MC）集成由于其灵活性和通用性而广泛用于现实图像合成中。但是，积分必须平衡估计器的偏差和方差，这会导致视觉上分散注意力的噪声以及较低的样本数。现有解决方案分为两类，过程中采样方案和后处理重建方案。该报告总结了后处理重建方案中的最新趋势。近年来，通过训练神经网络从稀疏的MC样本中重建降噪后的渲染结果，通过深度学习对MC渲染进行降噪已经引起了越来越多的关注并取得了重大进展。这些技术中的许多技术在实际应用中都显示出令人鼓舞的结果，本报告旨在为从业人员和研究人员提供这些方法的评估。