Machine learning for accelerated rendering

Abstract

This paper analyzes the application of machine learning methods to accelerate the rendering process in computer graphics. It examines key stages of the graphics pipeline and identifies computational challenges associated with traditional approaches such as rasterization and ray tracing. The study highlights modern neural rendering techniques, including neural radiance fields , adaptive sampling, and image denoising, which significantly improve performance and visual quality. Advantages such as reduced computational costs, real-time visualization, and improved photorealism are discussed alongside limitations related to training complexity and data requirements. The paper concludes that integrating machine learning with hardware acceleration technologies provides promising directions for efficient high-quality 3D rendering.