As-Global-As-Possible stereo matching with Sparse Depth Measurement Fusion

The recently lauded methodologies of As-Global-As-Possible (AGAP) and Sparse Depth Measurement Fusion (SDMF) have emerged as celebrated solutions for tackling the issue of stereo matching. AGAP addresses the congenital shortcomings of Semi-Global-Matching (SGM) in terms of streaking effects, while SDMF leverages active depth sensors to boost disparity computation. In this paper, these two methods are intertwined for attaining superior disparity estimation. Random sparse Depth measurements are fused with Diffusion-Based Fusion to update AGAP’s matching costs. Then, Neighborhood-Based Fusion refines the cost further, leveraging the previous results. Ultimately, the segment-based disparity refinement strategy is utilized for handling outliers and mismatched pixels to achieve final disparity results. Performance evaluations on various stereo datasets demonstrate that the proposed algorithm not only surpasses other challenging stereo matching algorithms but also achieves near real-time efficiency. It is worth pointing out that our proposal surprisingly outperforms most of the deep learning based stereo matching algorithms on Middlebury v.3 online evaluation system, despite not utilizing any learning-based techniques, further validating its superiority and practicality.