Machine Learning-Based Denoising Techniques for Monte Carlo Rendering: A Literature Review

Monte Carlo (MC) rendering is a powerful technique for achieving photorealistic images by simulating complex light interactions. However, the inherent noise introduced by MC rendering necessitates effective denoising techniques to enhance image quality. This paper presents a comprehensive review and comparative analysis of various machine learning (ML) methods for denoising MC renderings, focusing on four main categories: radiance prediction using convolutional neural networks (CNNs), kernel prediction networks, temporal rendering with recurrent architectures, and adaptive sampling approaches. Through systematic analysis of 7 peer-reviewed studies from 2019-2024, the author's findings reveal that deep learning models, particularly generative adversarial networks (GANs), achieve superior denoising performance. The study identifies key challenges including computational demands, with some methods requiring significant GPU resources, and generalization across diverse scenes. Additionally, we observe a trade-off between denoising quality and processing speed, particularly crucial for real-time applications. The study concludes with recommendations for future research, emphasizing the need for hybrid approaches combining physics- based models with ML techniques to improve robustness and efficiency in production environments.