One of the main goals of many augmented reality applications is to provide a seamless integration of a real scene with additional virtual data. To fully achieve that goal, such applications must typically provide high-quality real-world tracking, support real-time performance and handle the mutual occlusion problem, estimating the position of the virtual data into the real scene and rendering the virtual content accordingly. In this survey, we focus on the occlusion handling problem in augmented reality applications and provide a detailed review of 161 articles published in this field between January 1992 and August 2020. To do so, we present a historical overview of the most common strategies employed to determine the depth order between real and virtual objects, to visualize hidden objects in a real scene, and to build occlusion-capable visual displays. Moreover, we look at the state-of-the-art techniques, highlight the recent research trends, discuss the current open problems of occlusion handling in augmented reality, and suggest future directions for research.

Abstract In this paper, we present revectorization‐based soft shadow mapping, an algorithm that enables the rendering of visually plausible anti‐aliased soft shadows in real time. In revectorization‐based shadow mapping, shadow silhouettes are anti‐aliased and filtered on the basis of a discontinuity space. By replacing the filtering step of the theoretical framework of the percentage‐closer soft shadow algorithm by a revectorization‐based filtering algorithm, we are able to provide anti‐aliasing mainly for near contact shadows or small penumbra sizes generated from low‐resolution shadow maps. Moreover, we present a screen‐space variant of our technique that generates visually plausible soft shadows with an overhead of only in processing time, when compared to the fastest soft shadow algorithms proposed in the literature, but that introduces shadow overestimation artefacts in the final rendering.

Non-rigid surface registration is fundamental when accurate tracking or reconstruction of 3D deformable shapes is desirable. However, the majority of non-rigid registration methods are not as fast as the ones developed in the field of rigid registration. Fast methods for non-rigid surface registration are particularly interesting for markerless augmented reality applications, in which the object being used as marker can support non-rigid user interaction. In this paper, we present an adaptive algorithm for non-rigid surface registration. Taking advantage from this adaptivity and the parallelism of the GPU, we show that the proposed algorithm is capable to achieve near real-time performance with an approach as accurate as the ones proposed in the literature.