Jin Yusheng, Zhao Hong, Bu Penghui, Yan Jiaxing
Appl Opt. 2021 Oct 20;60(30):9578-9586. doi: 10.1364/AO.438830.
The computation of the disparity for the pixels in the weak texture area has always been a difficult task in stereo vision. The non-local method based on a minimum spanning tree (MST) provides a solution to construct content-adaptive support regions to perform cost aggregation. However, it always introduces error disparity in slanted surfaces and is sensitive to noise and highly textural regions. The window-based methods are not effective for information dissemination. To overcome the problem mentioned above, this paper proposes an approximate geodesic distance tree filter, which utilizes geodesic distance as a pixels similarity metric and recursive techniques to perform the filtering process. The filtering process is performed recursively in four directions (namely from top-left, top-right, and vice versa), which make our filter with linear complexity. Our filter has advantages in the sense that: (1) the pixel similarity metric is approximated geodesic distance; (2) the computational complexity is linear to the image pixel. Due to these reasons, the proposed method can properly cope with cost aggregation in the textureless regions and preserve the boundary of disparity maps. We demonstrate the strength of our filter in several applications.
在立体视觉中，计算弱纹理区域像素的视差一直是一项艰巨的任务。基于最小生成树（MST）的非局部方法提供了一种构建内容自适应支持区域以执行代价聚合的解决方案。然而，它总是在倾斜表面引入错误视差，并且对噪声和高纹理区域敏感。基于窗口的方法在信息传播方面效果不佳。为了克服上述问题，本文提出了一种近似测地距离树滤波器，它利用测地距离作为像素相似性度量，并采用递归技术来执行滤波过程。滤波过程在四个方向（即从左上角、右上角，反之亦然）上递归执行，这使得我们的滤波器具有线性复杂度。我们的滤波器具有以下优点：（1）像素相似性度量是近似测地距离；（2）计算复杂度与图像像素呈线性关系。由于这些原因，所提出的方法可以适当地处理无纹理区域中的代价聚合，并保留视差图的边界。我们在几个应用中展示了我们滤波器的优势。
