A Survey on Deep Learning for Monte Carlo Path Tracing

Abstract

Recent strides in hardware-accelerated ray tracing have propelled algorithms once deemed suitable only for offline rendering, like Monte Carlo path tracing, into interactive frame rates. While path tracing has been regarded as a practical utility in animating scenes for the film industry, achieving visually noise-free imagery often mandates thousands of samples per pixel and considerable computation time. Regrettably, this poses a difficulty for video games and virtual reality applications, which demand high frame rates and resolutions, thereby constraining the computational overhead of path tracing. Two extant approaches, in-process sampling, and post-processing reconstruction methods, i.e., denoising and upsampling, address this challenge. The giant evolution of deep learning technology has emerged as pivotal in path tracing processing. We explore and advance Monte Carlo path tracing technology based on deep learning. Moreover, we illustrate the merits and demerits of diverse designs and technologies, propose potential future development trends, and aim to provide researchers with a comprehensive understanding of the cutting-edge in deep learning-driven Monte Carlo path tracing.