Planar Markerless Augmented Reality Using Online Orientation Estimation

This paper presents a fast and accurate online orientation estimation method that estimates the normal direction of an arbitrary planar target from small baseline images using efficient bundle adjustment. The estimated normal direction is used for planar metric rectification, and the rectified target images are registered as the recognition targets for markerless tracking on the fly. The conventional planar metric rectification methods estimate the normal direction in a very efficient way, and recently proposed depth map estimation methods estimate accurate normal direction. However, they suffer from either poor estimation accuracy for small baseline images or high computational cost, which degrades the usability for untrained end-users in terms of shooting procedure or waiting time for new target registration. In contrast, the proposed method achieves both high estimation accuracy and fast processing speed in order to improve the usability, by reducing the number of degrees of freedom in bundle adjustment for the conventional depth map estimation methods. We compare the proposed method with these conventional methods using both artificially generated keypoints and real camera sequences with small baseline motion. The experimental results show the high estimation accuracy of the proposed method relative to the conventional planar metric rectification methods and significantly greater speed compared to the conventional depth map estimation methods, without sacrificing estimation accuracy.