Augmented Reality (AR) is a rapidly developing field with numerous potential applications. For example, building developers, public authorities, and other construction industry stakeholders need to visually assess potential new developments with regard to aesthetics, health and safety, and other criteria. Current state‐of‐the‐art visualization technologies are mainly fully virtual, while AR has the potential to enhance those visualizations by observing proposed designs directly within the real environment.
A novel AR system is presented, that is most appropriate for urban applications. It is based on monocular vision, is markerless, and does not rely on beacon‐based localization technologies (like GPS) or inertial sensors. Additionally, the system automatically calculates occlusions of the built environment on the augmenting virtual objects.
Three datasets from real environments presenting different levels of complexity (geometrical complexity, textures, occlusions) are used to demonstrate the performance of the proposed system. Videos augmented with our system are shown to provide realistic and valuable visualizations of proposed changes of the urban environment. Limitations are also discussed with suggestions for future work.
Most of the recent work on image-based object recognition and 3D reconstruction has focused on improving the underlying algorithms. In this paper we present a method to automatically improve the quality of the reference database, which, as we will show, also affects recognition and reconstruction performances significantly. Starting out from a reference database of clustered images we expand small clusters. This is done by exploiting cross-media information, which allows for crawling of additional images. For large clusters redundant information is removed by scene analysis. We show how these techniques make object recognition and 3D reconstruction both more efficient and more precise -we observed up to 14.8% improvement for the recognition task. Furthermore, the methods are completely data-driven and fully automatic.
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