Local stereo disparity estimation with novel cost aggregation for sub-pixel accuracy improvement in automotive applications

This paper presents a local disparity calculation algorithm on calibrated stereo images based on cost aggregation. Unlike most of the existing cost aggregation methods which are mainly based on the grouping of colour similarities, the proposed algorithm is grouped by local cost similarities. The proposed algorithm also applies a bilateral filter to enhance the normalised cost volume and, then, uses the winner-take-all technique to select the correspondence candidates. Finally, a quadratic polynomial interpolation is performed using the candidates and their neighbourhood values to achieve sub-pixel disparity resolution. The experimental results indicate that the proposed algorithm is able to provide dense disparity maps with sub-pixel resolution and achieves better accuracy compared to two similar stereo matching algorithms.