Semi-Global Matching (SGM) is a widely-used technique for dense image matching that is popular because of its accuracy and speed. While it works well for textured scenes, it can fail on slanted surfaces particularly in wide-baseline configurations due to the so-called fronto-parallel bias. In this paper, we propose an extension of SGM that utilizes image warping to reduce the fronto-parallel bias in the data term, based on estimating dominant slanted planes. The latter are also used as surface priors improving the smoothness term. Our proposed method calculates disparity maps for each dominant slanted plane and fuses them to obtain the final disparity map. We have quantitatively evaluated our approach outperforming SGM and SGM-P on synthetic data and demonstrate its potential on real data by qualitative results. In this way, we underscore the need to tackle the fronto-parallel bias in particular for wide-baseline configurations in both the data term and the smoothness term of SGM.    «

Semi-Global Matching (SGM) is a widely-used technique for dense image matching that is popular because of its accuracy and speed. While it works well for textured scenes, it can fail on slanted surfaces particularly in wide-baseline configurations due to the so-called fronto-parallel bias. In this paper, we propose an extension of SGM that utilizes image warping to reduce the fronto-parallel bias in the data term, based on estimating dominant slanted planes. The latter are also used as surface p...    »