Abstract:Although ray tracing produces high-fidelity, realistic images, it is considered computationally burdensome when implemented on a high rendering rate system. Perception-driven rendering methods produce images with slight noise and distortion that can be well-accepted by the human visual system and reduce rendering budgets. In this paper, we propose a perception-entropy-driven temporal reusing method to accelerate real-time ray tracing. We first build a just noticeable difference model to represent the uncertainty of ray samples and image space masking effects. Then, we expand the shading gradient through gradient max-pooling and gradient filtering to enlarge the visual receipt field. Finally, we dynamically optimize reusable time segments to improve the accuracy of temporal reusing. Compared with Monte Carlo ray tracing, our algorithm enhances FPS(frames per second) by 1.93x to 2.96x at 8 to 16 samples per pixel, significantly accelerating the Monte Carlo ray tracing process while maintaining visual quality.