State of the Art on Deep Learning-enhanced Rendering Methods  

作　　者：Qi Wang Zhihua Zhong Yuchi Huo Hujun Bao Rui Wang 

机构地区：[1]State Key Laboratory of CAD&CG,Zhejiang University,Hangzhou,310058,China [2]Zhejiang Laboratory,Hangzhou,311121,China

出　　处：《Machine Intelligence Research》2023年第6期799-821,共23页机器智能研究（英文版）

摘　　要：Photorealistic rendering of the virtual world is an important and classic problem in the field of computer graphics.With the development of GPU hardware and continuous research on computer graphics,representing and rendering virtual scenes has become easier and more efficient.However,there are still unresolved challenges in efficiently rendering global illumination effects.At the same time,machine learning and computer vision provide real-world image analysis and synthesis methods,which can be exploited by computer graphics rendering pipelines.Deep learning-enhanced rendering combines techniques from deep learning and computer vision into the traditional graphics rendering pipeline to enhance existing rasterization or Monte Carlo integration renderers.This state-of-the-art report summarizes recent studies of deep learning-enhanced rendering in the computer graphics community.Specifically,we focus on works of renderers represented using neural networks,whether the scene is represented by neural networks or traditional scene files.These works are either for general scenes or specific scenes,which are differentiated by the need to retrain the network for new scenes.

关 键 词：Neural rendering computer graphics scene representation RENDERING POST-PROCESSING 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]