Occlusion Handling in Outdoor Augmented Reality using a Combination of Map Data and Instance Segmentation

Visual consistency between virtual objects and the real environment is essential to improve user experience in Augmented Reality (AR). Occlusion handling is one of the key factors for maintaining visual consistency. In an application scenario for small areas such as indoors, various methods are applicable to acquire a depth information required for occlusion handling. However, in an application scenario in wide environment such as outdoor especially a scene including many buildings, occlusion handling is a challenging task because acquiring an accurate depth map is challenging. Several studies that have tackled this problem utilized 3D models of real buildings, but they have suffered from the accuracy of 3D models and camera localization. In this study, we propose a novel occlusion handling method using a monocular RGB camera and map data. Our method detects the regions of buildings in a camera image using an instance segmentation method and then obtains accurate occlusion handling in the image from each building instance and corresponding building map. The qualitative evaluation shows the improvement in the occlusion handling with buildings. The user study also shows the better performance of the perception of depth and distance than a model-based method.