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Authors: Maximilian Jarofka ; Stephan Schweig ; Niko Maas and Dieter Schramm

Affiliation: Chair of Mechatronics, University of Duisburg-Essen, Lotharstraße 1, 47057 Duisburg, Germany

ISBN: 978-989-758-444-2

ISSN: 2184-2841

Keyword(s): Artificial Neural Network, COLMAP, Clustering, Driving Simulator, Metashape, Meshroom, Object Classification, Object Detection, Photogrammetry, Process Chain, Unity, VisualSFM.

Abstract: This paper presents an automated process chain for the reconstruction of characteristic 3D objects, which can be used in a simulation environment. The process chain can distinguish between recurring objects such as trees and cars and specific objects like buildings. To acquire this, it detects and classifies objects in images from a previously recorded video. In contrast to the specific objects, which are reconstructed during the workflow of the process chain, the recurrent objects are loaded from already existing models and are placed multiple times into the simulation environment. In terms of quality a visual comparison between the two integrated programs for the reconstruction (Metashape and Meshroom) is carried out. Furthermore the accuracy of the positioning of standard objects in the Unity game engine is examined.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Jarofka, M.; Schweig, S.; Maas, N. and Schramm, D. (2020). Toolchain Development for Automated Scene Reconstruction using Artificial Neural Network Object Detection and Photogrammetry for the Application in Driving Simulators. In Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH, ISBN 978-989-758-444-2; ISSN 2184-2841, pages 25-34. DOI: 10.5220/0009590500250034

@conference{simultech20,
author={Maximilian Jarofka. and Stephan Schweig. and Niko Maas. and Dieter Schramm.},
title={Toolchain Development for Automated Scene Reconstruction using Artificial Neural Network Object Detection and Photogrammetry for the Application in Driving Simulators},
booktitle={Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH,},
year={2020},
pages={25-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009590500250034},
isbn={978-989-758-444-2},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH,
TI - Toolchain Development for Automated Scene Reconstruction using Artificial Neural Network Object Detection and Photogrammetry for the Application in Driving Simulators
SN - 978-989-758-444-2
IS - 2184-2841
AU - Jarofka, M.
AU - Schweig, S.
AU - Maas, N.
AU - Schramm, D.
PY - 2020
SP - 25
EP - 34
DO - 10.5220/0009590500250034

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