In this paper, we propose a method to select parameter values for stereo matching methods. The proposed method was trained in a supervised manner, and an evolutionary algorithm is used to select optimized parameter values for a given domain and a cost function constructed to measure the goodness level of candidate parameter values. Performance of the proposed method is compared to that of five current stereo matching methods, including the efficient large-scale stereo matching, belief propagation, semi-global block matching, stereo matching by training a convolutional neural network to compare image patches, and the efficient deep learning for stereo matching, for KITTI 2012, KITTI 2015, Middlebury, and EISAT datasets. The optimized parameters improve accuracy for all stereo matching methods considered, with some cases of improvement reaching up to \(24\%\). Source code and experimental results are available online.

中文翻译：

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立体对应的参数选择框架

在本文中，我们提出了一种为立体声匹配方法选择参数值的方法。所提出的方法是在监督下进行训练的，并且使用进化算法来选择给定域的优化参数值，并构造成本函数来测量候选参数值的优劣水平。将该方法的性能与目前的五种立体匹配方法进行了比较，包括高效的大规模立体匹配，置信传播，半全局块匹配，通过训练卷积神经网络比较图像斑块的立体匹配以及高效立体匹配的深度学习，适用于KITTI 2012，KITTI 2015，Middlebury和EISAT数据集。优化的参数可提高所有考虑的立体声匹配方法的准确性，\（24 \％\）。源代码和实验结果可在线获得。