Hirschmüller Heiko
German Aerospace Center (DLR), Institute of Robotics and Mechatronics, Muenchner Str. 20, 82234 Wessling, Germany.
IEEE Trans Pattern Anal Mach Intell. 2008 Feb;30(2):328-41. doi: 10.1109/TPAMI.2007.1166.
This paper describes the Semi-Global Matching (SGM) stereo method. It uses a pixelwise, Mutual Information based matching cost for compensating radiometric differences of input images. Pixelwise matching is supported by a smoothness constraint that is usually expressed as a global cost function. SGM performs a fast approximation by pathwise optimizations from all directions. The discussion also addresses occlusion detection, subpixel refinement and multi-baseline matching. Additionally, postprocessing steps for removing outliers, recovering from specific problems of structured environments and the interpolation of gaps are presented. Finally, strategies for processing almost arbitrarily large images and fusion of disparity images using orthographic projection are proposed.A comparison on standard stereo images shows that SGM is among the currently top-ranked algorithms and is best, if subpixel accuracy is considered. The complexity is linear to the number of pixels and disparity range, which results in a runtime of just 1-2s on typical test images. An in depth evaluation of the Mutual Information based matching cost demonstrates a tolerance against a wide range of radiometric transformations. Finally, examples of reconstructions from huge aerial frame and pushbroom images demonstrate that the presented ideas are working well on practical problems.
本文描述了半全局匹配（SGM）立体匹配方法。它使用基于互信息的逐像素匹配代价来补偿输入图像的辐射差异。逐像素匹配由通常表示为全局代价函数的平滑约束支持。SGM通过从所有方向进行路径优化来执行快速近似。讨论还涉及遮挡检测、亚像素细化和多基线匹配。此外，还介绍了用于去除异常值、从结构化环境的特定问题中恢复以及间隙插值的后处理步骤。最后，提出了处理几乎任意大图像的策略以及使用正交投影融合视差图像的方法。在标准立体图像上的比较表明，SGM是当前排名靠前的算法之一，如果考虑亚像素精度，则是最佳算法。其复杂度与像素数量和视差范围呈线性关系，这导致在典型测试图像上的运行时间仅为1 - 2秒。对基于互信息的匹配代价的深入评估表明，它能容忍广泛的辐射变换。最后，从大型航空帧图像和推扫式图像进行重建的示例表明，所提出的方法在实际问题上效果良好。
