Register Based on Large Scene for Augmented Reality System

Zhen-Wen Gui,


Register is steadily gaining in importance due to the drive from various computer vision applications, such as augmented reality (AR), mobile computing, and human-machine interface. Efficient keypoint-based approachs are widely used in scene register. These approaches often model a scene as a collection of keypoints and associated descriptors, and then construct a set of correspondences between scene and image keypoints via descriptor matching . Finally, these correspondences are used as input to a robust geometric estimation algorithm such as RANSAC to find the transformation of the scene in the image. This paper focuses on designing a robust and flexible registration method for wide-area augmented reality applications. Firstly, we propose to partition the whole scene into several sub-maps according to the user’s preference or the requirements of the AR applications instead of building a global map of the wide-area scene.
Secondly a linear structured SVM classifier is used to perform scene learning online, which allows us to quickly adapt our model to a given environment. Finally, a hybrid tracking strategy is implemented by combining both wide and narrow baseline techniques. Some experiments have been conducted to demonstrate the validity of our methods.

Citation Format:
Zhen-Wen Gui, "Register Based on Large Scene for Augmented Reality System," Journal of Internet Technology, vol. 21, no. 1 , pp. 99-111, Jan. 2020.

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