Model reconstruction and pose acquisition using extended Lowe's method

Abstract

Finding the pose and structure of an unknown object from an image sequence has many applications in graphics, virtual reality, and multimedia processing. In this paper, we address this problem by using a two-stage iterative method. Starting from an initial guess of the structure, the first stage estimates the pose of the object. The second stage uses the estimated pose information to refine the structure. This process is repeated until the difference between the observed data and data re-projected from the estimated model is minimized. This method is a variation of the classical bundle adjustment method, but is faster in execution and is simpler to implement. We used the Kanade-Lucas-Tomasi feature tracker for obtaining the image features. Synthetic and real data have been tested with good results.