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In recent years, techniques for accelerating rendering by exploiting the limitations of the human visual system have become increasingly prevalent. The foveated rendering method significantly reduces the computational requirements during rendering by reducing image quality in peripheral regions. In this paper, we propose a scene‐content‐sensitive real‐time adaptive foveated rendering method. First, we pre‐render the three‐dimensional (3D) scene at a low resolution. Then, we utilize the low‐resolution pre‐rendered image as input to extract edge, local contrast, and color features. Subsequently, we generate a screen‐space region division map based on the gaze point position. Next, we calculate the visual importance of each 16 × 16 pixel tile based on edge, local contrast, color, and screen‐space region. We then map the visual importance to the shading rate to generate a shading rate control map for the current frame. Finally, we complete the rendering of the current frame based on variable rate shading technology. Experimental results demonstrate that our method effectively enhances the visual quality of images near the foveal region while generating high quality foveal region images. Furthermore, our method can significantly improve performance compared to per‐pixel shading method and existing scene‐content‐based foveated rendering methods. [ABSTRACT FROM AUTHOR]

Occlusion handling is a key technical issue in augmented reality research. This paper proposes a new occlusion algorithm based on object contour detection to address issues such as poor real-time occlusion processing, high computational complexity in comparing the depth values of virtual and real objects, and the presence of jagged, blurry, and hollow edges in occluded areas. First, based on the depth and color information, we obtained aligned images of real scenes. Second, we extracted the maximum closed contour of the real object in the scene and overlaid it with the aligned image. Subsequently, we generated a virtual object and obtained a depth map of the virtual object. Finally, by comparing the depth values of the stacked images with the virtual objects, masks are generated in real time and optimized to present the occlusion processing results. Experimental comparisons demonstrated that the algorithm presented in this study not only improves real-time performance but also enhances accuracy at the intersection edges of virtual and real images. Simultaneously, it is no longer limited by the size of real scene images and can achieve real-time virtual and real occlusion effects. [ABSTRACT FROM AUTHOR]

Achieving the dual carbon goal is a profound and challenging social transformation. The difficulty is mainly reflected in the fact that economic growth is usually linked to energy consumption. In today's society, no country or industry can completely abandon traditional energy, and the use of traditional energy, namely fossil fuels, would bring carbon emissions. Therefore, reducing carbon emissions while ensuring economic growth and achieving the dual carbon goals is an important issue at present. This article conducted a series of carbon emission analysis and carbon reduction path research for power generation enterprises, which are the major emitters of carbon emissions, based on the background of dual carbon goals.