Weighting linear matching for stereo vision

Recently, the local stereo matching algorithms based on the adaptive weighting achieve very accurate disparity maps. Compared to global matching approaches, the local algorithms offer less complexities. However, these methods are still beyond hardware ability for real-time application. In this paper, a novel linear stereo matching algorithm with constant execution time is proposed. To begin with, a weighting linear cost aggregation is introduced based on the Weighting Guided Image Filtering (WGIF) model which can avoid halo artifacts in local filters. Similar to image filtering, `Halos' may change edge distribution in disparity map. Moreover, a complete stereo processing pipeline including cost computation, cost aggregation, disparity selection and disparity refinement is constructed. Experimental results shows that the proposed approach is effective and efficient in stereo matching. Compared to other state-of-the-art local algorithms, our approach achieves comparable results while performs better at occlusion edges.