Triple-SGM: Stereo Processing using Semi-Global Matching with Cost Fusion

In this work, we propose an extension of the Semi-Global Matching framework for three images from a stereo rig consisting of a horizontal and vertical camera pair. After calculating the matching costs separately for both image pairs, these are merged at cost level using cubic spline interpolation. For cost values near the left/bottom image boundaries, we propose an advanced weighting strategy. Subsequently, the fused matching can be used directly for the cost aggregation and disparity estimation. The benefits of the proposed fusion strategy are demonstrated by an evaluation based on synthetic and real-world data. To encourage further comparisons on triple stereo algorithms, the dataset used for evaluation is made publicly available.