Learning to Detect Ground Control Points for Improving the Accuracy of Stereo Matching

Aristotle Spyropoulos, N. Komodakis, Philippos Mordohai
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引用次数: 108

Abstract

While machine learning has been instrumental to the ongoing progress in most areas of computer vision, it has not been applied to the problem of stereo matching with similar frequency or success. We present a supervised learning approach for predicting the correctness of stereo matches based on a random forest and a set of features that capture various forms of information about each pixel. We show highly competitive results in predicting the correctness of matches and in confidence estimation, which allows us to rank pixels according to the reliability of their assigned disparities. Moreover, we show how these confidence values can be used to improve the accuracy of disparity maps by integrating them with an MRF-based stereo algorithm. This is an important distinction from current literature that has mainly focused on sparsification by removing potentially erroneous disparities to generate quasi-dense disparity maps.
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学习检测地面控制点以提高立体匹配精度
虽然机器学习在计算机视觉的大多数领域都取得了长足的进步,但它还没有被应用于类似频率或成功的立体匹配问题。我们提出了一种监督学习方法,用于基于随机森林和一组捕获关于每个像素的各种形式信息的特征来预测立体匹配的正确性。我们在预测匹配的正确性和置信度估计方面显示了高度竞争的结果,这使我们能够根据其分配差异的可靠性对像素进行排序。此外,我们展示了如何将这些置信度值与基于磁磁共振成像的立体算法相结合,从而提高视差图的精度。这是与当前文献的一个重要区别,当前文献主要关注通过消除潜在的错误差异来生成准密集差异图的稀疏化。
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