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

Autor: Ahmed Rida Sekkat, Yohan Dupuis, Varun Ravi Kumar, Hazem Rashed, Senthil Yogamani, Pascal Vasseur, Paul Honeine
Přispěvatelé: Honeine, Paul
Rok vydání: 2022
Předmět:
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
FOS: Computer and information sciences
Control and Optimization
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing
Mechanical Engineering
Fisheye Cameras
Computer Vision and Pattern Recognition (cs.CV)
Biomedical Engineering
[INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]
[STAT.ML] Statistics [stat]/Machine Learning [stat.ML]
Computer Science Applications
Human-Computer Interaction
[INFO.INFO-CY] Computer Science [cs]/Computers and Society [cs.CY]
[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Artificial Intelligence
Control and Systems Engineering
Computer Vision and Pattern Recognition
Omnidirectional vision
Automated Driving
Synthetic Datasets
[MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
DOI: 10.48550/arxiv.2203.05056
Popis: 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{\deg} field of view cover the 360{\deg} 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.
Comment: IEEE Robotics and Automation Letters (RA-L) and IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022). An initial sample of the dataset is released in https://drive.google.com/drive/folders/1N5rrySiw1uh9kLeBuOblMbXJ09YsqO7I
Databáze: OpenAIRE
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