This work provides a comprehensive analysis of the evolution of rendering techniques in computer graphics, focusing on the transition from rasterization, a method renowned for its efficiency, to physically based rendering techniques such as ray tracing and path tracing, which offer superior visual realism. Despite the unmatched accuracy of ray tracing in simulating complex light interactions, its widespread adoption has historically been limited by its high computational demands and sampling noise. Recent breakthroughs in deep learning have catalyzed a paradigm shift in rendering workflows, offering practical solutions to these challenges. Neural networks, through intelligent denoising, adaptive sampling strategies, and novel acceleration architectures, are now significantly enhancing both the performance and quality of ray tracing. This technological synergy is becoming increasingly critical to meet the escalating demand for high-fidelity, real-time graphics in fields such as gaming, virtual production, and immersive simulations. The report evaluates the transformative impact of deep learning on ray tracing and its implications for the future of interactive digital media.
Dahiwal et al. (Wed,) studied this question.