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The OmniScape Dataset

Ahmed Rida Sekkat 1, 2 Yohan Dupuis 3 Pascal Vasseur 2 Paul Honeine 1 
1 DocApp - LITIS - Equipe Apprentissage
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
2 STI - LITIS - Equipe Systèmes de Transport Intelligent
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
Abstract : Despite the utility and benefits of omnidirectional images in robotics and automotive applications, there are no datasets of omnidirectional images available with semantic segmentation, depth map, and dynamic properties. This is due to the time cost and human effort required to annotate ground truth images. This paper presents a framework for generating omnidirectional images using images that are acquired from a virtual environment. For this purpose, we demonstrate the relevance of the proposed framework on two well-known simulators: CARLA Simulator, which is an open-source simulator for autonomous driving research, and Grand Theft Auto V (GTA V), which is a very high quality video game. We explain in details the generated OmniScape dataset, which includes stereo fisheye and catadioptric images acquired from the two front sides of a motorcycle, including semantic segmentation, depth map, intrinsic parameters of the cameras and the dynamic parameters of the motorcycle. It is worth noting that the case of two-wheeled vehicles is more challenging than cars due to the specific dynamic of these vehicles.
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Contributor : Paul Honeine Connect in order to contact the contributor
Submitted on : Saturday, December 26, 2020 - 12:46:51 AM
Last modification on : Thursday, March 10, 2022 - 10:46:02 AM
Long-term archiving on: : Monday, March 29, 2021 - 4:41:18 PM


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Ahmed Rida Sekkat, Yohan Dupuis, Pascal Vasseur, Paul Honeine. The OmniScape Dataset. 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, Paris, France. pp.1603-1608, ⟨10.1109/ICRA40945.2020.9197144⟩. ⟨hal-03088300⟩



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