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.

我们提出了一种方法，用于在立体匹配问题中估计视差置信区间。置信区间提供了与常规置信度量互补的信息。据我们所知，这是第一种基于代价体积创建视差置信区间的方法。该方法依赖于可能性分布来解释代价体积的认知不确定性。我们的方法具有白盒特性，在这方面与当前最先进的深度神经网络方法不同。使用 Middlebury 立体数据集以及卫星图像数据集验证了置信区间的准确性和大小。此贡献在 GitHub 上免费提供。