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Active Stereo Reconstruction using Deep Learning
Linköping University, Department of Biomedical Engineering.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Depth estimation using stereo images is an important task in many computer vision applications. A stereo camera contains two image sensors that observe the scene from slightly different viewpoints, making it possible to find the depth of the scene. An active stereo camera also uses a laser projector that projects a pattern into the scene. The advantage of the laser pattern is the additional texture that gives better depth estimations in dark and textureless areas. 

Recently, deep learning methods have provided new solutions producing state-of-the-art performance in stereo reconstruction. The aim of this project was to investigate the behavior of a deep learning model for active stereo reconstruction, when using data from different cameras. The model is self-supervised, which solves the problem of having enough ground truth data for training the model. It instead uses the known relationship between the left and right images to let the model learn the best estimation.

The model was separately trained on datasets from three different active stereo cameras. The three trained models were then compared using evaluation images from all three cameras. The results showed that the model did not always perform better on images from the camera that was used for collecting the training data. However, when comparing the results of different models using the same test images, the model that was trained on images from the camera used for testing gave better results in most cases.

Place, publisher, year, edition, pages
2019. , p. 52
Keywords [en]
Computer Vision, Deep Learning, Stereo Reconstruction
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-158276ISRN: LIU-IMT-TFK-A--19/569--SEOAI: oai:DiVA.org:liu-158276DiVA, id: diva2:1332305
External cooperation
SICK Linköping
Subject / course
Electrical Engineering
Presentation
2019-06-17, Algoritmen, B-huset, Campus Valla, Linköping, 09:07 (English)
Supervisors
Examiners
Available from: 2019-06-28 Created: 2019-06-28 Last updated: 2019-06-28Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
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  • de-DE
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  • en-US
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  • nn-NB
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  • Other locale
More languages
Output format
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