Occlusion Handling Algorithm Based on Contour Detection

Abstract

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.