Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on binocular computer vision systems that meet stringent response time and accuracy requirements. A key problem in these vision systems is stereo matching, which involves matching pixels from two input images in order to construct the output, a 3D map. Building upon the existing local stereo matching algorithms, this paper proposes a novel stereo matching algorithm that is based on a weighted guided filtering foundation. The proposed algorithm consists of three main steps; each step is designed with the goal of improving accuracy. First, the matching costs are computed using a unique combination of complementary methods (absolute difference, Census, and gradient algorithms) to reduce errors. Second, the costs are aggregated using an adaptive weighted guided image filtering method. Here, the regularization parameters are adjusted adaptively using the Canny method, further reducing errors. Third, a disparity map is generated using the winner-take-all strategy; this map is subsequently refined using a densification method to reduce errors. Our experimental results indicate that the proposed algorithm provides a higher level of accuracy in comparison to a collection of the existing state-of-the-art local algorithms.

中文翻译：

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局部立体匹配：一种基于自适应加权引导图像过滤的方法

在机器人导航和自动（无人驾驶）车辆等快速发展的领域中的创新应用依赖于满足严格响应时间和精度要求的双目计算机视觉系统。这些视觉系统中的一个关键问题是立体匹配，它涉及匹配来自两个输入图像的像素以构建输出，即 3D 地图。在现有的局部立体匹配算法的基础上，本文提出了一种基于加权引导滤波的立体匹配算法。所提出的算法包括三个主要步骤；每一步的设计都是为了提高准确性。首先，使用互补方法（绝对差异、人口普查和梯度算法）的独特组合来计算匹配成本，以减少错误。第二，使用自适应加权引导图像过滤方法汇总成本。在这里，使用 Canny 方法自适应调整正则化参数，进一步减少错误。第三，使用赢者通吃策略生成视差图；随后使用致密化方法对该地图进行细化以减少错误。我们的实验结果表明，与现有最先进的局部算法的集合相比，所提出的算法提供了更高水平的准确度。