  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.
