This paper presents a novel augmented reality (AR) framework specifically targeting scene-based exhibits. Unlike traditional AR libraries that rely on specific image targets, the proposed framework utilizes a bag-of-words model for scene recognition, which enables recognition and subsequent launch of AR experiences under challenging environments. Moreover, the proposed framework utilizes relocalization capabilities of the popular ORB-SLAM algorithm to track the user’s movement after recognition of a particular exhibit. We demonstrate the efficacy of the proposed framework through a complete mobile app designed for a local museum, where artifacts are enhanced through AR.
CITATION STYLE
Li-Chee-Ming, J., Wu, Z., Tan, R., Tan, R., Khan, N. M., Ye, A., & Guan, L. (2019). A scene-based augmented reality framework for exhibits. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11662 LNCS, pp. 287–296). Springer Verlag. https://doi.org/10.1007/978-3-030-27202-9_26
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