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 journalArticlepeer-review

9 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
  • Stereo vision
  • confidence
  • occupancy grids
  • stereo matching
  • uncertainty

ASJC Scopus subject areas

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


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