线性立体匹配

Leonardo De-Maeztu, S. Mattoccia, A. Villanueva, R. Cabeza
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引用次数: 102

摘要

近年来基于自适应权重策略的局部立体匹配算法达到了与全局匹配方法相似的精度。这些算法的一个主要问题是它们的计算成本很高,而且这种复杂性随着窗口大小的增加而成比例地增加。本文提出了一种新的成本聚合步骤,其复杂性与窗口大小无关(即O(1)),优于最先进的O(1)方法。此外,与其他O(1)方法相比,我们的方法不依赖于积分直方图,可以使用彩色图像而不是灰度图像进行聚合。最后,为了改进算法的结果,还提出了视差细化管道。整体算法产生的结果可与最先进的立体匹配算法相媲美。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Linear stereo matching
Recent local stereo matching algorithms based on an adaptive-weight strategy achieve accuracy similar to global approaches. One of the major problems of these algorithms is that they are computationally expensive and this complexity increases proportionally to the window size. This paper proposes a novel cost aggregation step with complexity independent of the window size (i.e. O(1)) that outperforms state-of-the-art O(1) methods. Moreover, compared to other O(1) approaches, our method does not rely on integral histograms enabling aggregation using colour images instead of grayscale ones. Finally, to improve the results of the proposed algorithm a disparity refinement pipeline is also proposed. The overall algorithm produces results comparable to those of state-of-the-art stereo matching algorithms.
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