Symmetric dynamic programming stereo using block matching guidance

In this paper, three stereo matching algorithms are investigated: Block Matching Stereo (BMS) represents local area matching techniques, Symmetric Dynamic Programming Stereo (SDPS) represents semi-global matching, and Graph Cuts Stereo (GCS) represents global matching. Both local and semiglobal methods are relatively fast and feasible for parallel implementation. On the other hand, global GCS obtains a higher stereo matching accuracy but it is very computationally expensive. Thus, we propose a technique that guides SDPS using pre-computed Local-based BMS's result which both restricts and guides the Dynamic Programming search for optimal profile related to BMS's signals. The technique is not only parallel-able for a faster processing speed but also self-repeatable for an enhancement of the 3D reconstruction. Our experimental results show that this proposed technique is superior to the Global Graph Cuts Stereo (GCS) in both speed and accuracy. As well as reconstructing relatively accurate results from the infamous Middlebury datasets, this method is also demonstrated to be fast, robust, and reliable in reconstructing high quality 3D scenes from real-life stereo images.