Lightweight Method for Two-Dimensional Object Registration for Augmented Reality

The purpose of this study is to develop an indoor guidance method based on augmented reality (AR) that superimposes annotations on an indoor environment (e.g., wall surfaces) and works on devices with limited computing power such as smartphones. Such a method requires a simple algorithm for the object registration. For the purpose, this paper proposes a two-dimensional (2D) object registration method based on perspective distortion correction using vanishing points in the scene and a stable vanishing point detection method. Current vanishing point detection methods have the problem of slight drift of the vanishing point in the videos despite maintaining a fixed camera position. The proposed method has solved the problem by employing a weighted particle filtering method. Recent approaches often employ deep neural networks (DNNs) for the registration and the stable vanishing point detection, however DNNs require rich resources for computing such as Graphics Processing Unit (GPU). Since the proposed method requires at most two vanishing points for the registration, the method can be said a lightweight method. This paper shows that the proposed weighted particle filtering method suppresses the vanishing point drift better than the conventional moving average method, and that it enables registration of objects based on the method.