Confidence Estimation for Superpixel-Based Stereo Matching

Rafael Gouveia, Aristotle Spyropoulos, Philippos Mordohai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

In this paper we propose an approach for estimating the confidence of stereo matches for super pixel-based disparity estimation. To our knowledge, this is the first such method reported in the literature. Starting from a simple super pixel stereo algorithm, we present a representative set of features that can be extracted from the disparity map and the super pixel fitting process. A random forest classifier is then trained on these features to predict whether the disparity assigned to each pixel of a test disparity map is correct or not. We perform experiments on the KITTI stereo benchmark and show that our confidence estimator is very accurate in predicting which disparities are correct and which are not. We also present a post-processing algorithm for improving the accuracy of the disparity maps that exploits the confidence estimates to reject wrong disparity values and achieves significant error reduction.

Original languageEnglish
Title of host publicationProceedings - 2015 International Conference on 3D Vision, 3DV 2015
EditorsMichael Brown, Jana Kosecka, Christian Theobalt
Pages180-188
Number of pages9
ISBN (Electronic)9781467383325
DOIs
StatePublished - 20 Nov 2015
Event2015 International Conference on 3D Vision, 3DV 2015 - Lyon, France
Duration: 19 Oct 201522 Oct 2015

Publication series

NameProceedings - 2015 International Conference on 3D Vision, 3DV 2015

Conference

Conference2015 International Conference on 3D Vision, 3DV 2015
Country/TerritoryFrance
CityLyon
Period19/10/1522/10/15

Keywords

  • Accuracy
  • Benchmark testing
  • Estimation
  • Feature extraction
  • Measurement uncertainty
  • Three-dimensional displays
  • Training

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