产品级渲染中的机器学习
Survey: Machine Learning in Production Rendering
摘要：
在过去的几年里，基于机器学习的方法在渲染动画长片方面取得了一些巨大的成功。本调查总结了使用深度神经网络相对于传统渲染方法的几个最显著的改进，例如更好的图像质量和更低的计算开销。更具体地说，本调查涵盖了机器学习的基本原理及其应用，例如去噪，路径引导，渲染参与媒体以及其他众所周知的困难光传输情况。其中一些技术已经在最新发布的动画中使用，而其他技术仍在学术界和电影制片厂的研究人员的持续开发中。虽然基于学习的渲染方法仍然存在一些问题，但它们已经在渲染管道的多个部分表现出了良好的性能，人们正在不断进行新的尝试。
英文摘要：
In the past few years, machine learning-based approaches have had some great success for rendering animated feature films. This survey summarizes several of the most dramatic improvements in using deep neural networks over traditional rendering methods, such as better image quality and lower computational overhead. More specifically, this survey covers the fundamental principles of machine learning and its applications, such as denoising, path guiding, rendering participating media, and other notoriously difficult light transport situations. Some of these techniques have already been used in the latest released animations while others are still in the continuing development by researchers in both academia and movie studios. Although learning-based rendering methods still have some open issues, they have already demonstrated promising performance in multiple parts of the rendering pipeline, and people are continuously making new attempts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
