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Deep learning-based real-time ray tracing technology in games
- 1 Dundee International Institution, Central South University, Changsha, China
* Author to whom correspondence should be addressed.
Abstract
In recent years, deep learning-based techniques have revolutionized real-time ray tracing for gaming, significantly enhancing visual fidelity and rendering performance. This paper reviews various state-of-the-art methods, including the use of Generative Adversarial Networks (GANs) for realistic shading, the use of neural temporal adaptive sampling, the use of subpixel sampling reconstruction, and the use of neural scene representation. Key findings highlight improvements in perceived realism, temporal stability, image fidelity, and computational efficiency. Techniques such as neural intersection functions and spatiotemporal reservoir resampling further optimize rendering speed and memory usage. Additionally, approaches like adaptive sampling and neural denoising using layer embeddings contribute to reduced noise and enhanced image clarity. Collectively, these advancements make real-time ray tracing more feasible for high-fidelity gaming applications, offering enhanced graphics without compromising performance. These improvements are significant. My analysis underscores the critical role of deep learning in overcoming traditional ray tracing challenges, paving the way for more immersive and responsive gaming experiences. Furthermore, these innovations suggest a promising future for integrating advanced ray tracing techniques into a broader range of interactive media, ensuring both visual excellence and operational efficiency.
Keywords
Deep learning, Real-time ray tracing, Image processing, Computer graphics.
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Cite this article
Peng,S. (2024). Deep learning-based real-time ray tracing technology in games. Applied and Computational Engineering,101,124-131.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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