Deep learning caustic image generation

Caustic patterns-intricate light structures formed by reflection and refraction-have broad applications in rendering, design, and optics. Traditional caustic synthesis methods, such as solving Poisson's equation or iterative ray tracing, are computationally expensive and scale poorly with complexity. We propose a data-driven framework that replaces explicit optimization with machine learning. A neural network is trained to learn the mapping between transparent object geometry, illumination, and resulting caustic distributions. The model efficiently generates high-resolution, physically plausible caustic images in real time. Results highlight its ability to generalize across complex scenarios, offering a practical alternative for graphics and optical design.