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VarCity - the video: the struggles and triumphs of leveraging fundamental research results in a graphics video production

Published: 30 July 2017 Publication History

Abstract

VarCity - the Video is a short documentary-style CGI movie explaining the main outcomes of the 5-year Computer Vision research project VarCity. Besides a coarse overview of the research, we present the challenges that were faced in its production, induced by two factors: i) usage of imperfect research data produced by automatic algorithms, and ii) human factors, like federating researchers and a CG artist around a similar goal many had a different conception of, while no one had a detailed overview of all the content. Successive achievement was driven by some ad-hoc technical developments but more importantly of detailed and abundant communication and agreement on common best practices.

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MP4 File (talks-0345.mp4)

References

[1]
Filip Biljecki, Jantien Stoter, Hugo Ledoux, Sisi Zlatanova, and Arzu Çöltekin. 2015. Applications of 3D City Models: State of the Art Review. ISPRS International Journal of Geo-Information 4, 4 (2015), 2842--2889.
[2]
András Bódis-Szomorú, Hayko Riemenschneider, and Luc Van Gool. 2016. Efficient Volumetric Fusion of Airborne and Street-Side Data for Urban Reconstruction. In International Conference on Pattern Recognition. Cancun, Mexico.
[3]
Michael Gygli, Helmut Grabner, Hayko Riemenschneider, and Luc Van Gool. 2014. Creating Summaries from User Videos. In European Conference on Computer Vision. Zürich, Switzerland.
[4]
Santiago Manen, Michael Gygli, Dengxin Dai, and Luc Van Gool. 2017. PathTrack: Fast Trajectory Annotation with Path Supervision. CoRR abs/1703.02437 (2017). htp://arxiv.org/abs/1703.02437
[5]
Hayko Riemenschneider, András Bódis-Szomorú, Julien Weissenberg, and Luc Van Gool. 2014. Learning Where To Classify In Multi-View Semantic Segmentation. In European Conference on Computer Vision. Zürich, Switzerland.

Cited By

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  • (2023)CCTV-Calib: a toolbox to calibrate surveillance cameras around the globeMachine Vision and Applications10.1007/s00138-023-01476-134:6Online publication date: 20-Oct-2023
  • (2022)Improving Depth Estimation Using Map-Based Depth PriorsIEEE Robotics and Automation Letters10.1109/LRA.2022.31469147:2(3640-3647)Online publication date: Apr-2022
  • (2018)A Volumetric Fusing Method for TLS and SFM Point CloudsIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2018.285690011:9(3349-3357)Online publication date: Sep-2018

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Published In

cover image ACM Conferences
SIGGRAPH '17: ACM SIGGRAPH 2017 Talks
July 2017
158 pages
ISBN:9781450350082
DOI:10.1145/3084363
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 July 2017

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Author Tags

  1. computer vision
  2. fundamental research
  3. general-public

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Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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Cited By

View all
  • (2023)CCTV-Calib: a toolbox to calibrate surveillance cameras around the globeMachine Vision and Applications10.1007/s00138-023-01476-134:6Online publication date: 20-Oct-2023
  • (2022)Improving Depth Estimation Using Map-Based Depth PriorsIEEE Robotics and Automation Letters10.1109/LRA.2022.31469147:2(3640-3647)Online publication date: Apr-2022
  • (2018)A Volumetric Fusing Method for TLS and SFM Point CloudsIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2018.285690011:9(3349-3357)Online publication date: Sep-2018

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