Stereo processing by Semiglobal Matching and Mutual Information
Stereo processing by Semiglobal Matching and Mutual Information
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DOI：
10.1109/tpami.2007.1166
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发表时间：
2008-02-01
影响因子：
23.6
通讯作者：
Hirschmueller, Heiko
中科院分区：
文献类型：
--
作者：
Hirschmueller, Heiko
本文介绍了SGM立体匹配方法。它使用一个像素级的，互信息（MI）为基础的匹配成本，用于补偿输入图像的辐射差异。像素匹配由平滑度约束支持，该平滑度约束通常表示为全局成本函数。SGM通过从各个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，后处理步骤去除离群值，从结构化环境的特定问题中恢复，以及间隙的插值。最后，提出了利用正交投影处理几乎任意大图像和融合视差图像的策略。对标准立体图像的比较表明，SGM是目前排名靠前的算法之一，是最好的，如果考虑到亚像素精度。复杂度与像素数量和视差范围呈线性关系，这导致典型测试图像的运行时间仅为1-2秒。基于MI的匹配成本的深入评估表明了对广泛的辐射变换的容忍度。最后，通过对大幅面航空影像和推扫式影像的重建实例，验证了本文提出的方法在实际问题中的有效性。
This paper describes the Semiglobal Matching ( SGM) stereo method. It uses a pixelwise, Mutual Information (MI)-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 multibaseline 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 1-2 seconds on typical test images. An in depth evaluation of the MI-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.