论文标题
级别设置的立体声，用于遮挡合作分组
Level Set Stereo for Cooperative Grouping with Occlusion
论文作者
论文摘要
本地化立体声边界很困难，因为在与之相邻的封闭区域中不存在匹配提示。我们引入了一个能量和级别的优化器，该优化器通过编码遮挡的基本几何形状来改善边界：遮挡的空间范围必须等于导致它的差异跳跃的幅度。在米德尔伯里（Middlebury）和立体声数据集（Stereo Dataset）掉落事物的图形场景集合中，该模型比以前的咬合处理技术提供了更准确的边界。
Localizing stereo boundaries is difficult because matching cues are absent in the occluded regions that are adjacent to them. We introduce an energy and level-set optimizer that improves boundaries by encoding the essential geometry of occlusions: The spatial extent of an occlusion must equal the amplitude of the disparity jump that causes it. In a collection of figure-ground scenes from Middlebury and Falling Things stereo datasets, the model provides more accurate boundaries than previous occlusion-handling techniques.
