Author(s): Wang, C (Wang, Chen); Wang, X (Wang, Xiang); Zhang, JW (Zhang, Jiawei); Zhang, L (Zhang, Liang); Bai, X (Bai, Xiao); Ning, X (Ning, Xin); Zhou, J (Zhou, Jun); Hancock, E (Hancock, Edwin)

Source: PATTERN RECOGNITION Volume: 124 Article Number: 108498 DOI: 10.1016/j.patcog.2021.108498 Published: APR 2022

Abstract: Although deep learning-based stereo matching approaches have achieved excellent performance in recent years, it is still a non-trivial task to estimate the uncertainty of the produced disparity map. In this paper, we propose a novel approach to estimate both aleatoric and epistemic uncertainties for stereo matching in an end-to-end way. We introduce an evidential distribution, named Normal Inverse-Gamma (NIG) distribution, whose parameters can be used to calculate the uncertainty. Instead of directly regressed from aggregated features, the uncertainty parameters are predicted for each potential disparity and then averaged via the guidance of matching probability distribution. Furthermore, considering the sparsity of ground truth in real scene datasets, we design two additional losses. The first one tries to enlarge uncertainty on incorrect predictions, so uncertainty becomes more sensitive to erroneous regions. The second one enforces the smoothness of the uncertainty in the regions with smooth disparity. Most stereo matching models, such as PSM-Net, GA-Net, and AA-Net, can be easily integrated with our approach. Experiments on multiple benchmark datasets show that our method improves stereo matching results. We prove that both aleatoric and epistemic uncertainties are well-calibrated with incorrect predictions. Particularly, our method can capture increased epistemic uncertainty on out-of-distribution data, making it effective to prevent a system from potential fatal consequences. Code is available at https://github.com/Dawnstar8411/StereoMatching-Uncertainty . (c) 2021 Elsevier Ltd. All rights reserved.

Accession Number: WOS:000736972200014

Author Identifiers:

Author Web of Science ResearcherID ORCID Number

Hancock, Edwin C-6071-2008 0000-0003-4496-2028

ISSN: 0031-3203

eISSN: 1873-5142

Full Text: https://www.sciencedirect.com/science/article/pii/S0031320321006749?via%3Dihub