Multi-frame stereo matching with edges, planes, and superpixels
Multi-frame stereo matching with edges, planes, and superpixels
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DOI：
10.1016/j.imavis.2019.05.006
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Tianfan Xue;Andrew Owens;D. Scharstein;M. Goesele;R. Szeliski
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文献类型：
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作者：
Tianfan Xue;Andrew Owens;D. Scharstein;M. Goesele;R. Szeliski
提出了一种基于多帧图像边缘提取和匹配的多帧窄基线立体匹配算法。边缘匹配使我们能够在一开始就专注于重要的特征，并处理遮挡边界以及无纹理区域。给定初始稀疏匹配，我们拟合重叠的局部平面以形成场景的粗略的、过完整的表示。在将序列中的参考图像分解为超像素后，我们执行马尔可夫随机场优化，将每个超像素分配给其中一个平面假设。最后，我们使用分段连续变分优化来改进我们的连续深度图估计。我们的方法成功地处理了深度不连续性，遮挡和大型无纹理区域，同时还生成了详细而准确的深度图。我们表明，我们的方法在高分辨率多帧立体基准上优于竞争方法，非常适合于视图插值应用。
We present a multi-frame narrow-baseline stereo matching algorithm based on extracting and matching edges across multiple frames. Edge matching allows us to focus on the important features at the very beginning, and deal with occlusion boundaries as well as untextured regions. Given the initial sparse matches, we fit overlapping local planes to form a coarse, over-complete representation of the scene. After breaking up the reference image in our sequence into superpixels, we perform a Markov random field optimization to assign each superpixel to one of the plane hypotheses. Finally, we refine our continuous depth map estimate using a piecewise-continuous variational optimization. Our approach successfully deals with depth discontinuities, occlusions, and large textureless regions, while also producing detailed and accurate depth maps. We show that our method out-performs competing methods on high-resolution multi-frame stereo benchmarks and is well-suited for view interpolation applications.