Fast and robust feature tracking for 3D reconstruction

Abstract

Feature tracking as one of the most important components of 3D reconstruction based on structure from motion (SFM) has attracted a wide range of attention from computer vision community. However, existing feature tracking methods often suffer from image distort, scale-change and varying lighting, then resulting many incorrect matches, namely outliers. Meanwhile, these methods are very costly to calculate. To defend this drawback, a fast and robust feature tracking (FRFT) is proposed for 3D reconstruction with SFM. Firstly, to save computational cost, the feature clustering method is used to cluster a big volume of image collection to some small ones to avoid some undesirable feature matches. Secondly, the union find set (UFS) method is used to achieve fast feature matching, this can furtherly save computation time of feature tracking. Thirdly, a geometry-constraint method is proposed to remove outlier from tracks produced by feature tracking method. Finally, a comprehensive evaluation is conducted to assess the proposed FRFT and the state-of-the-art methods. Experimental results show that the proposed FRFT method has the best performance on both efficiency and effectiveness.