A fast segmentation-driven algorithm for accurate stereo correspondence

Recent cost aggregation strategies that adapt their weights to image content enabled local algorithms to obtain results comparable to those of global algorithms based on more complex disparity optimization methods. Unfortunately, despite the potential advantages in terms of memory footprint and algorithmic simplicity compared to global algorithms, most of the state-of-the-art cost aggregation strategies deployed in local algorithms are extremely slow. In fact, their execution time is comparable and often worse than those of global approaches. In this paper we propose a framework for accurate and fast cost aggregation based on segmentation that allows us to obtain results comparable to state-of-the-art approaches much more efficiently (the execution time drops from minutes to seconds). A further speed-up is achieved taking advantage of multi-core capabilities available nowadays in almost any processor. The comparison with state-of-the-art cost aggregation strategies highlights the effectiveness of our proposal.