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  Connecting the Dots: Learning Representations for Active Monocular Depth Estimation

Riegler, G., Liao, Y., Donne, S., Koltun, V., & Geiger, A. (2019). Connecting the Dots: Learning Representations for Active Monocular Depth Estimation. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019) (pp. 7616-7625). Piscataway, NJ: IEEE. doi:10.1109/CVPR.2019.00781.

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Genre: Conference Paper

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 Creators:
Riegler, Gernot1, Author
Liao, Yiyi2, Author           
Donne, Simon2, Author           
Koltun, Vladlen1, Author
Geiger, Andreas2, Author           
Affiliations:
1External Organizations, ou_persistent22              
2Max Planck Research Group Autonomous Vision, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_2344692              

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Free keywords: Forschungsgruppe Geiger
 Abstract: -

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Language(s): eng - English
 Dates: 2020-01-092019
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Riegler2019CVPR
DOI: 10.1109/CVPR.2019.00781
 Degree: -

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Title: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)
Place of Event: Long Beach, CA
Start-/End Date: 2019-06-15 - 2019-06-20

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Title: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)
  Other : 2019 IEEE/CVP Conference on Computer Vision and Pattern Recognition (CVPR 2019)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Piscataway, NJ : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 7616 - 7625 Identifier: ISBN: 978-1-7281-3293-8
ISBN: 978-1-7281-3294-5
ISSN: 2575-7075
ISSN: 1063-6919
DOI: 10.1109/CVPR41558.2019