Related papers: Robust Confidence Intervals in Stereo Matching using Possibility Theory

Robust Confidence Intervals in Stereo Matching using Possibility Theory

URL: http://arxiv.org/abs/2404.06273v1
Date: Tue, 9 Apr 2024 12:48:24 GMT
Title: Robust Confidence Intervals in Stereo Matching using Possibility Theory
Authors: Roman Malinowski, Emmanuelle Sarrazin, Loïc Dumas, Emmanuel Dubois, Sébastien Destercke,
Abstract summary: We propose a method for estimating disparity confidence intervals in stereo matching problems. To the best of our knowledge, this is the first method creating disparity confidence intervals based on the cost volume. The accuracy and size of confidence intervals are validated using the Middlebury stereo datasets as well as a dataset of satellite images.
Score: 2.522402937703098
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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