Efficient pose tracking on mobile phones with 3D points grouping

With the rapid growth of computational capability and popularity of mobile phones, Mobile Augmented Reality (MAR) in large scale 3D scenes becomes an emerging field in recent years. The core of MAR is to continuously compute a precise 6 Degree-of-Freedom (DOF) camera pose for each frame, i.e. localization. However, as a crucial part of localization, the 2D-3D points matching is usually inefficient due to the usage of traditional features, e.g. SIFT, and the large number of 3D points candidates. This paper aims to tackle this problem by designing an efficient 6DOF pose tracking system on mobile phone. In this system, binary features are used in both offline sparse reconstruction and online tracking, while a PCA-based 3D points partition method is proposed to reduce the searching space of 2D-3D points matching, making it capable to achieve a low computational cost. Experiments on a NOKIA N900 smartphone show that our system could efficiently and robustly estimate the 6DOF camera pose.