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Propagating Confidences through CNNs for Sparse Data Regression
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-3292-7153
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Inception Institute of Artificial Intelligence Abu Dhabi, UAE.
2019 (English)In: British Machine Vision Conference 2018, BMVC 2018, BMVA Press , 2019Conference paper, Published paper (Refereed)
Abstract [en]

In most computer vision applications, convolutional neural networks (CNNs) operate on dense image data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open problem with numerous applications in autonomous driving, robotics, and surveillance. To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. Furthermore, we propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. Comprehensive experiments are performed on the KITTI depth benchmark and the results clearly demonstrate that the proposed approach achieves superior performance while requiring three times fewer parameters than the state-of-the-art methods. Moreover, our approach produces a continuous pixel-wise confidence map enabling information fusion, state inference, and decision support.

Place, publisher, year, edition, pages
BMVA Press , 2019.
National Category
Computer Vision and Robotics (Autonomous Systems) Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-149648OAI: oai:DiVA.org:liu-149648DiVA, id: diva2:1233027
Conference
The 29th British Machine Vision Conference (BMVC), Northumbria University, Newcastle upon Tyne, England, UK, 3-6 September, 2018
Available from: 2018-07-13 Created: 2018-07-13 Last updated: 2020-02-03Bibliographically approved

Open Access in DiVA

Propagating Confidences through CNNs for Sparse Data Regression(5993 kB)52 downloads
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Eldesokey, AbdelrahmanFelsberg, MichaelKhan, Fahad Shahbaz

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Eldesokey, AbdelrahmanFelsberg, MichaelKhan, Fahad Shahbaz
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Output format
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