A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
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DOI：
10.1023/a:1014573219977
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发表时间：
2002-04-01
影响因子：
19.5
通讯作者：
Szeliski, R
中科院分区：
文献类型：
--
作者：
Scharstein, D;Szeliski, R
立体匹配是计算机视觉中最活跃的研究领域之一。虽然已经开发了大量的立体对应算法，但对其性能进行表征的工作相对较少。在本文中，我们提出了密集的分类，两帧立体方法。我们的分类法的目的是评估不同的组件和设计决策在个别立体算法。使用这种分类法，我们比较了现有的立体方法和目前的实验，评估了许多不同变体的性能。为了建立一个通用的软件平台和便于评估的数据集集合，我们设计了一个独立的、灵活的c++实现，可以对单个组件进行评估，并且可以很容易地扩展到包括新算法。我们还制作了几个新的多帧立体数据集，并将代码和数据集放到网上。最后，我们对一组目前表现最好的立体算法进行了比较评估。
Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods. Our taxonomy is designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms. We have also produced several new multi-frame stereo data sets with ground truth and are making both the code and data sets available on the Web. Finally, we include a comparative evaluation of a large set of today's best-performing stereo algorithms.