SynWoodScape: Synthetic Surround-View Fisheye Camera Dataset for Autonomous Driving

Ahmed Rida Sekkat (1, 2) , Yohan Dupuis (3) , Varun Ravi Kumar (4) , Hazem Rashed (4) , Senthil Yogamani (5) , Pascal Vasseur (6) , Paul Honeine (2)

1 STI - LITIS - Equipe Systèmes de Transport Intelligent
2 App - LITIS - Equipe Apprentissage
3 LINEACT - Laboratoire d'Innovation Numérique pour les Entreprises et les Apprentissages au service de la Compétitivité des Territoires
4 Valeo DAR - Valeo Valeo Driver Assistance Research Kronach
5 Valeo Vision Systems [Galway]
6 MIS - Modélisation, Information et Systèmes - UR UPJV 4290

Surround-view cameras are a primary sensor for automated driving, used for near-field perception. It is one of the most commonly used sensors in commercial vehicles primarily used for parking visualization and automated parking. Four fisheye cameras with a 190 °field of view cover the 360 °around the vehicle. Due to its high radial distortion, the standard algorithms do not extend easily. Previously, we released the first public fisheye surround-view dataset named WoodScape. In this work, we release a synthetic version of the surround-view dataset, covering many of its weaknesses and extending it. Firstly, it is not possible to obtain ground truth for pixel-wise optical flow and depth. Secondly, WoodScape did not have all four cameras annotated simultaneously in order to sample diverse frames. However, this means that multi-camera algorithms cannot be designed to obtain a unified output in birds-eye space, which is enabled in the new dataset. We implemented surround-view fisheye geometric projections in CARLA Simulator matching WoodScape’s configuration and created SynWoodScape. We release 80k images from the synthetic dataset with annotations for 10+ tasks. We also release the baseline code and supporting scripts.

Mots clés: Fisheye Cameras; Omnidirectional vision; Automated Driving; Synthetic Datasets

Domaines: Machine Learning [stat.ML]; Traitement du signal et de l'image [eess.SP]; Statistiques [math.ST]; Traitement du signal et de l'image [eess.SP]; Réseau de neurones [cs.NE]; Apprentissage [cs.LG]; Ordinateur et société [cs.CY]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Ahmed Rida Sekkat, Yohan Dupuis, Varun Ravi Kumar, Hazem Rashed, Senthil Yogamani, et al.. SynWoodScape: Synthetic Surround-View Fisheye Camera Dataset for Autonomous Driving. IEEE Robotics and Automation Letters, 2022, 7 (3), pp.8502-8509. ⟨10.1109/LRA.2022.3188106⟩. ⟨hal-03749088⟩