Collaborative semi-global stereo matching

Abstract

Efficiency and accuracy of semi-global matching (SGM) make it outperform many stereo matching algorithms and is widely used under challenging occasions. However, SGM only incorporates information along a scanline in each pass and lacks interaction between scanlines, resulting in streak artifacts in the disparity image. We introduce a local edge-aware filtering method to SGM to enhance the interaction of neighboring scanlines, since streak artifacts can be avoided. We use bilateral weights based on intensity similarity and spatial affinity between pixels to build connections among scanlines. In each pass, we recursively estimate the aggregated cost of SGM and compute the weighted average of aggregated costs for pixels in the orthogonal direction to obtain the output of our method along each scanline. As one-dimensional bilateral filtering is used in our method, the extra computation is linear to image resolution and label space, which is a small fraction of that needed by SGM. We present ablation studies using stereo pairs under both constrained and natural conditions to verify the effectiveness of our method. Extensive experiments on Middlebury and Karlsruhe Institute of Technology and Toyota Technology Institute datasets demonstrate that our method removes all streak artifacts, improves the quality of the disparity image, and outperforms many other non-local cost aggregation approaches.

Research on the Improvement of Semi-Global Matching Algorithm for Binocular Vision Based on Lunar Surface Environment

“…In response, Liu et al [ 29 ] propose a method of layering and precise positioning of the disparity search space using SGM pyramids and local invariant features to improve computational efficiency, reduce memory occupation, and reduce the influence of similar textures. Bu et al [ 30 ] introduced a local edge-aware filtering method in SGM to enhance the interaction of adjacent scanlines and avoid fringe artifacts. Deng et al [ 31 ] combined multi-path cost aggregation and cross-scale cost aggregation (CSCA) to propose an SGM-MSIF algorithm based on multi-scale information fusion (MSIF).…”