Stereo depth estimation is error-prone; hence, effective error detection methods are desirable. Most such existing methods depend on characteristics of the stereo matching cost curve, making them unduly dependent on functional details of the matching algorithm. As a remedy, we propose a novel error detection approach based solely on the input image and its depth map. Our assumption is that, entropy of any point on an image will be significantly higher than the entropy of its corresponding point on the image's depth map. In this paper, we propose a confidence measure, Entropy-Difference (ED) for stereo depth estimates and a binary classification method to identify incorrect depths. Experiments on the Middlebury dataset show the effectiveness of our method. Our proposed stereo confidence measure outperforms 17 existing measures in all aspects except occlusion detection. Established metrics such as precision, accuracy, recall, and area-under-curve are used to demonstrate the effectiveness of our method.

翻译：系统深度估计容易出错; 因此, 有效的误差探测方法是可取的。 多数现有方法取决于立体比对成本曲线的特性, 使得它们不适当地依赖于匹配算法的功能细节 。 作为补救措施, 我们提议了一种新颖的错误探测方法, 仅以输入图像及其深度地图为基础。 我们的假设是, 图像上任何点的增缩将大大高于图像深度地图上相应点的增缩。 在本文中, 我们提议了一种信任度量度( Entropy- difference (ED), 用于立体深度估计, 以及一种二进制分类方法, 以识别不正确的深度 。 在Middrobury 数据集上进行的实验显示了我们的方法的有效性 。 我们提议的立体信任度比除隔离探测外所有方面现有的17项措施都好。 我们使用精确度、 准确度、 回溯溯和区域正下曲线等既定指标来显示我们的方法的有效性 。