Level-of-Detail AR: Managing Points of Interest for Attentive Augmented Reality

Abstract

In this paper, we present level-of-detail (LOD) augmented reality (AR), which is a novel approach to handling multi-layered information of the target image. Previously, multitarget recognition and tracking methods were used to handle augmentation in a complex scene. In more complex situations, when the target can be divided into depth-based layers, it is not feasible to simply employ multi-target methods. To overcome this problem, we propose a tree structure of points of interest (POI) and a practical method that identifies the parts that attract maximum user attention. We demonstrate the feasibility of our approach by implementing a mobile LOD AR system that handles very large targets that are commonly encountered in real-world situations such as in museums.