LS-ELAS: Line segment based efficient large scale stereo matching
LS-ELAS: Line segment based efficient large scale stereo matching
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DOI：
10.1109/icra.2017.7989019
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发表时间：
2017-05
期刊：
影响因子：
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通讯作者：
Radouane Ait Jellal;Manuel Lange;B. Wassermann;A. Schilling;A. Zell
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作者：
Radouane Ait Jellal;Manuel Lange;B. Wassermann;A. Schilling;A. Zell
我们提出了 LS-ELAS，这是 ELAS 算法的线段扩展，它提高了性能和鲁棒性。 LS-ELAS 是一种双目密集立体匹配算法，它在恒定时间内计算图像中大多数像素的视差，并在线性时间内计算一小部分像素（支持点）的视差。我们的方法是基于线段来确定支撑点，而不是在图像范围内统一选择它们。通过这种方式，我们可以找到信息丰富的支撑点，从而保持深度不连续性。我们的贝叶斯立体匹配方法的先验基于一组线段和一组支撑点。这两个集合都被赋予受约束的 Delaunay 三角剖分，以生成意识到可能的深度不连续性的三角剖分网格。我们通过使用自适应方法沿边缘段采样候选点来进一步提高准确性。我们在 Middlebury 基准测试中评估了我们的算法。
We present LS-ELAS, a line segment extension to the ELAS algorithm, which increases the performance and robustness. LS-ELAS is a binocular dense stereo matching algorithm, which computes the disparities in constant time for most of the pixels in the image and in linear time for a small subset of the pixels (support points). Our approach is based on line segments to determine the support points instead of uniformly selecting them over the image range. This way we find very informative support points which preserve the depth discontinuity. The prior of our Bayesian stereo matching method is based on a set of line segments and a set of support points. Both sets are given to a constrained Delaunay triangulation to generate a triangulation mesh which is aware of possible depth discontinuities. We further increased the accuracy by using an adaptive method to sample candidate points along edge segments. We evaluated our algorithm on the Middlebury benchmark.