We propose a method for estimating disparity confidence intervals in stereo matching problems. Confidence intervals provide complementary information to usual confidence measures. To the best of our knowledge, this is the first method creating disparity confidence intervals based on the cost volume. This method relies on possibility distributions to interpret the epistemic uncertainty of the cost volume. Our method has the benefit of having a white-box nature, differing in this respect from current state-of-the-art deep neural networks approaches. The accuracy and size of confidence intervals are validated using the Middlebury stereo datasets as well as a dataset of satellite images. This contribution is freely available on GitHub.

翻译：我们提出了一种在立体匹配问题中估计视差置信区间的方法。置信区间为常规置信度测量提供了补充信息。据我们所知，这是首个基于代价体创建视差置信区间的方法。该方法利用可能性分布来解释代价体的认知不确定性。与当前最先进的深度神经网络方法不同，我们的方法具有白盒特性。置信区间的准确性和大小通过Middlebury立体数据集以及一组卫星图像数据集进行了验证。该贡献已在GitHub上免费公开。