Structural Similarity Measurement Based Cost Function for Stereo Matching of Automotive Applications

Abstract

The human visual perception uses structural information to recognize stereo correspondences in natural scenes. Therefore, structural information is important to build an efficient stereo matching algorithm. In this paper, we demonstrate that incorporating the structural information similarity, extracted either from image intensity (SSIM) directly or from image gradients (GSSIM), between two patches can accurately describe the patch structures and, thus, provides more reliable initial cost values. We also address one of the major phenomenons faced in stereo matching for real world scenes, radiometric changes. The performance of the proposed cost functions was evaluated within two stages: the first one considers these costs without aggregation process while the second stage uses the fast adaptive aggregation technique. The experiments were conducted on the real road traffic scenes KITTI 2012 and KITTI 2015 benchmarks. The obtained results demonstrate the potential merits of the proposed stereo similarity measurements under radiometric changes.

Point-to-Block Matching in Depth Estimation

“…Study [Weg09] has shown that a mixture of census or rank transforms with the simple sum of absolute differences (SAD) or sum of squared differences (SSD) can be very efficient for such multiview sequences. [Zeg20] proves that the use of structural similarity performs very well when radiometric distortions are present in processed input views.…”