In stereo matching, occlusion disparity refinement is one of the challenges when attempting to improve disparity accuracy. In order to refine the disparity in occluded regions, a geometric prior guided adaptive label search method and sequential disparity filling strategy are proposed. In our method, considering the scene structural correlation between pixels, the geometric prior information such as image patch similarity, matching distance, and disparity constraint is used in the proposed label search energy function and the disparity labels are searched by superpixel matching. Thus, the reliable disparity labels are adaptively searched and propagated for occlusion filling. In order to improve the accuracy in large occluded regions, by using the proposed sequential filling strategy, occluded regions are decomposed into multiple blocks and filled in multiple steps from the periphery; thus, reliable labels are iteratively propagated to the interior of occluded regions without violating the smooth disparity assumption. Experimental results on the Middlebury V3 benchmark show that, compared with other state-of-the-art algorithms, the proposed method achieves better disparity results under multiple criteria. The proposed method can provide better disparity refinement for typical stereo matching algorithms.

中文翻译：

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基于几何先验的自适应标签搜索的立体匹配遮挡视差细化

在立体匹配中，遮挡视差细化是尝试提高视差精度时的挑战之一。为了细化遮挡区域的视差，提出了一种几何先验引导的自适应标签搜索方法和顺序视差填充策略。在我们的方法中，考虑到像素之间的场景结构相关性，在提出的标签搜索能量函数中使用诸如图像块相似性、匹配距离和视差约束等几何先验信息，并通过超像素匹配来搜索视差标签。因此，可靠的视差标签被自适应地搜索和传播以进行遮挡填充。为了提高大遮挡区域的精度，通过使用提出的顺序填充策略，遮挡区域分解成多个块，从外围多步填充；因此，可靠的标签被迭代地传播到被遮挡区域的内部，而不会违反平滑视差假设。在 Middlebury V3 基准上的实验结果表明，与其他最先进的算法相比，所提出的方法在多个标准下取得了更好的视差结果。所提出的方法可以为典型的立体匹配算法提供更好的视差细化。所提出的方法在多个标准下取得了更好的视差结果。所提出的方法可以为典型的立体匹配算法提供更好的视差细化。所提出的方法在多个标准下取得了更好的视差结果。所提出的方法可以为典型的立体匹配算法提供更好的视差细化。