Fast local stereo matching using two-level adaptive cost filtering

Recent local stereo correspondence algorithms achieve accurate results by performing effective cost aggregation. In this paper, we solve the cost aggregation problem in the view of cost-volume filtering. A novel concept named “two-level local adaptation” is introduced to guide the proposed filtering approach. Also, a novel post-processing method is proposed to handle both occlusions and textureless regions. The improvement of performance is confirmed by applying it to the proposed stereo correspondence algorithm. The overall method generates competitive results, and outperforms methods that use the similar filtering technique. By implementing the entire algorithm on the GPU, it can achieve about 10 frames/s for typical stereo pairs with a resolution of 640×360 and a disparity range of 20 pixels.