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Normalized Convolution Network and Dataset Generation for Refining Stereo Disparity Maps
Linköping University, Department of Electrical Engineering, Computer Vision.
Linköping University, Department of Electrical Engineering, Computer Vision.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Finding disparity maps between stereo images is a well studied topic within computer vision. While both classical and machine learning approaches exist in the literature, they frequently struggle to correctly solve the disparity in regions with low texture, sharp edges or occlusions. Finding approximate solutions to these problem areas is frequently referred to as disparity refinement, and is usually carried out separately after an initial disparity map has been generated.

In the recent literature, the use of Normalized Convolution in Convolutional Neural Networks have shown remarkable results when applied to the task of stereo depth completion. This thesis investigates how well this approach performs in the case of disparity refinement. Specifically, we investigate how well such a method can improve the initial disparity maps generated by the stereo matching algorithm developed at Saab Dynamics using a rectified stereo rig.

To this end, a dataset of ground truth disparity maps was created using equipment at Saab, namely a setup for structured light and the stereo rig cameras. Because the end goal is a dataset fit for training networks, we investigate an approach that allows for efficient creation of significant quantities of dense ground truth disparities.

The method for generating ground truth disparities generates several disparity maps for every scene measured by using several stereo pairs. A densified disparity map is generated by merging the disparity maps from the neighbouring stereo pairs. This resulted in a dataset of 26 scenes and 104 dense and accurate disparity maps.

Our evaluation results show that the chosen Normalized Convolution Network based method can be adapted for disparity map refinement, but is dependent on the quality of the input disparity map.

Place, publisher, year, edition, pages
2019. , p. 82
Keywords [en]
Disparity Map, Disparity Refinement, Dataset Generation, Neural Network, Normalized Convolution
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-158449ISRN: LiTH-ISY-EX--19/5252--SEOAI: oai:DiVA.org:liu-158449DiVA, id: diva2:1333176
External cooperation
Saab Dynamics
Subject / course
Computer Vision Laboratory
Presentation
2019-06-18, Algoritmen, VALLA, B-Huset, Ingång 27, Linköping, 09:15 (Swedish)
Supervisors
Examiners
Available from: 2019-07-01 Created: 2019-06-30 Last updated: 2019-07-01Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • Other style
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  • de-DE
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  • Other locale
More languages
Output format
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  • asciidoc
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