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Karakottas, A., Zioulis, N., Zarpalas, D. and Daras, P. (2018) 360D: A Dataset and Baseline for Dense Depth Estimation from 360 Images. 1st Workshop on 360o Perception and Interaction, European Conference on Computer Vision (ECCV), Munich, Germany, 8-14 September 2018, 1-4.

has been cited by the following article:

• TITLE: Hyper 360—Towards a Unified Tool Set Supporting Next Generation VR Film and TV Productions

  AUTHORS: Barnabas Takacs, Zsuzsanna Vincze, Hannes Fassold, Antonis Karakottas, Nikolaos Zioulis, Dimitrios Zarpalas, Petros Daras

  KEYWORDS: Deep Learning, Tensor Flow, YoloV3, 360˚ Video, Virtual Reality, Free Viewpoint Video, Quality Control, Automatic Cinematography, Multi-Camera Systems

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.12 No.5, May 27, 2019

  ABSTRACT: We describe four fundamental challenges that complex real-life Virtual Reality (VR) productions are facing today (such as multi-camera management, quality control, automatic annotation with cinematography and 360˚depth estimation) and describe an integrated solution, called Hyper 360, to address them. We demonstrate our solution and its evaluation in the context of practical productions and present related results.