Exploration and Real-time Rendering Optimization Path Using Deep Learning Methods

With the continuous development of computer graphics, real- time rendering is increasingly being used in multimedia, virtual reality, and game development. How to improve rendering efficiency and quality is an important way to improve rendering effects, but current rendering methods often encounter performance bottlenecks when rendering complex scenes . In response to the above issues, this paper intends to use deep learning methods to study real-time rendering algorithms based on deep learning. This paper intends to use GAN (Generative Adversarial Networks) technology to automatically generate optimal graphics resources from the training model, thereby shortening rendering time, improving rendering quality, modeling rendering data in different scenes, and significantly reducing rendering costs while ensuring rendering effects. This paper can study an adaptive learning and adjustment mechanism that can dynamically adjust rendering parameters in different software and hardware environments to achieve better results. In Scenario 1, the SSIM (Structural Similarity) of the traditional method is 0.80, while the GAN method is improved to 0.88. The research results of this paper can provide new ideas and methods for optimizing real- time rendering technology.