Robust non-local stereo matching for outdoor driving images using segment-simple-tree

• We propose a non-local stereo matching algorithm for driver assistance systems.
• The disparity characteristics of outdoor driving images are demonstrated by analysis.
• We introduce segment-simple-tree that is more adequate for outdoor driving images than minimum spanning tree.
• Qualitative and quantitative evaluation to existing methods is provided over three datasets.

Non-local cost aggregation has recently emerged as a promising approach for stereomatching and has attracted much interest over the past few years. Most non-local algorithms are reportedly better than state-of-the-art local algorithms for high-quality indoor images. However, the accuracy of non-local algorithms is still limited for outdoor images. Computing disparity maps for outdoor images in driver assistance systems is one of the most actively researched topics in the field of stereo vision. In this paper, we present a robust non-local stereo matching algorithm that improves the performance of non-local approaches for outdoor driving images. The proposed algorithm is inspired by the non-local cost aggregation method based on a minimum spanning tree, and it improves the estimation accuracy by introducing an alternate, effective segment-simple-tree that is more adequate for outdoor driving images than the minimum spanning tree. Experimental results showed that the proposed algorithm is superior to the existing local and non-local algorithms, and is comparable to semi-global matching.