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

研究成果: Article査読

6 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号7112529
ページ(範囲)116-128
ページ数13
ジャーナルIEEE Transactions on Pattern Analysis and Machine Intelligence
38
1
DOI
出版ステータスPublished - 2016 1 1

ASJC Scopus subject areas

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

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