On Stereo Confidence Measures for Global Methods: Evaluation, New Model and Integration into Occupancy Grids

Martim Brandao, Ricardo Ferreira, Kenji Hashimoto, Atsuo Takanishi, Jose Santos-Victor

    Research output: Contribution to journalArticle

    5 Citations (Scopus)


    Stereo confidence measures are important functions for global reconstruction methods and some applications of stereo. In this article we evaluate and compare several models of confidence which are defined at the whole disparity range. We propose a new stereo confidence measure to which we call the Histogram Sensor Model (HSM), and show how it is one of the best performing functions overall. We also introduce, for parametric models, a systematic method for estimating their parameters which is shown to lead to better performance when compared to parameters as computed in previous literature. All models were evaluated when applied to two different cost functions at different window sizes and model parameters. Contrary to previous stereo confidence measure benchmark literature, we evaluate the models with criteria important not only to winner-take-all stereo, but also to global applications. To this end, we evaluate the models on a real-world application using a recent formulation of 3D reconstruction through occupancy grids which integrates stereo confidence at all disparities. We obtain and discuss our results on both indoors' and outdoors' publicly available datasets.

    Original languageEnglish
    Article number7112529
    Pages (from-to)116-128
    Number of pages13
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Issue number1
    Publication statusPublished - 2016 Jan 1


    • 3D reconstruction
    • confidence
    • occupancy grids
    • stereo matching
    • Stereo vision
    • uncertainty

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Vision and Pattern Recognition
    • Software
    • Computational Theory and Mathematics
    • Applied Mathematics

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