Recently, the extraction efficiency and quality of depth data have become the major concern in stereo vision. This work presents stereo depth estimation method based on Bidirectional Matching Method (BM ) aiming at achieving a good speed-accuracy trade-off. In this study, a novel bidirectional matching process is proposed for matching cost computation. Additionally, a robust cost function is designed for computing color, gradient and local binary pattern similarity between potential matching points. Next, an efficient adaptive spatially smooth edge-preserving filter is applied to minimize matching ambiguities. Following this, a Knock-Out Disparity Selection Technique (KO-DST) that iteratively solves a sequence of discrete binary optimization problem is used to generate disparity map. Finally, a discontinuity preserving disparity refinement is applied to correct disparity errors and refine the resulting disparity map. Rigorous experiments and analysis on Middlebury datasets validated the effectiveness of the presented idea and its superiority over some state-of-the-art algorithms. The proposed method runs approximately two times faster than the standard bidirectional matching. GRAPHICAL ABSTRACT

中文翻译：

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一种基于双向匹配的立体视觉系统深度估计新方法

最近，深度数据的提取效率和质量已成为立体视觉的主要关注点。这项工作提出了基于双向匹配方法（BM）的立体深度估计方法，旨在实现良好的速度-精度权衡。在这项研究中，提出了一种新的双向匹配过程来计算匹配成本。此外，设计了一个强大的成本函数来计算潜在匹配点之间的颜色、梯度和局部二元模式相似性。接下来，应用高效的自适应空间平滑边缘保留滤波器来最小化匹配模糊度。在此之后，使用迭代解决一系列离散二元优化问题的敲除视差选择技术 (KO-DST) 来生成视差图。最后，应用不连续性保留视差细化来纠正视差错误并细化产生的视差图。对 Middlebury 数据集的严格实验和分析验证了所提出想法的有效性及其相对于一些最先进算法的优越性。所提出的方法的运行速度大约是标准双向匹配的两倍。图形概要