AMBIGUITY CONCEPT IN STEREO MATCHING PIPELINE

In a 3D reconstruction pipeline, stereo matching step aims at computing a disparity map representing the depth between image pair. The evaluation of the disparity map can be done through the estimation of a confidence metric. In this article, we propose a new confidence metric, named ambiguity integ...

Full description

Saved in:
Bibliographic Details
Published inInternational archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLIII-B2-2021; pp. 383 - 390
Main Authors Sarrazin, E., Cournet, M., Dumas, L., Defonte, V., Fardet, Q., Steux, Y., Jimenez Diaz, N., Dubois, E., Youssefi, D., Buffe, F.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Gottingen Copernicus GmbH 28.06.2021
Copernicus GmbH (Copernicus Publications)
Copernicus Publications
Subjects
Ambiguity
Computer Science
Image Processing
Image reconstruction
Matching
Optimization
Pipelines
Quality assessment
Regularization
Submarine pipelines
Online AccessGet full text

Cover

More Information
Summary:In a 3D reconstruction pipeline, stereo matching step aims at computing a disparity map representing the depth between image pair. The evaluation of the disparity map can be done through the estimation of a confidence metric. In this article, we propose a new confidence metric, named ambiguity integral metric, to assess the quality of the produced disparity map. This metric is derived from the concept of ambiguity, which characterizes the property of the cost curve profile. It aims to quantify the difficulty in identifying the correct disparity to select. The quality of ambiguity integral metric is evaluated through the ROC curve methodology and compared with other confidence measures. In regards to other measures, the ambiguity integral measure shows a good potential. We also integrate this measure through various steps of the stereo matching pipeline in order to improve the performance estimation of the disparity map. First, we include ambiguity integral measure during the Semi Global Matching optimization step. The objective is to weight, by ambiguity integral measure, the influence of points in the SGM regularization to reduce the impact of ambiguous points. Secondly, we use ambiguity as an input of a disparity refinement deep learning architecture in order to easily locate noisy area and preserve details.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLIII-B2-2021-383-2021