@inproceedings{22.iros.SynWoodScape,
   author =  "Ahmed Rida Sekkat and Yohan Dupuis and Varun Ravi Kumar and Hazem Rashed and Senthil Yogamani and Pascal Vasseur and Paul Honeine",
   title =  "{SynWoodScape}: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving",
   booktitle =  "Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)",
   year  =  "2022",
   month = "20~" # oct,
   url_link = {https://arxiv.org/abs/2203.05056},
   url_paper = {https://arxiv.org/pdf/2203.05056.pdf},
   keywords  =  "Fisheye Cameras, Omni-directional Vision, Automated Driving, Synthetic Datasets",
   abstract = "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. 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 simultaneously in order to sample diverse frames. However, this means that multi-camera algorithms cannot be designed, 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."
}

{"_id":"pjDdZYRMGvMuTE7St","bibbaseid":"sekkat-dupuis-kumar-rashed-yogamani-vasseur-honeine-synwoodscapesyntheticsurroundviewfisheyecameradatasetforautonomousdriving-2022","author_short":["Sekkat, A. R.","Dupuis, Y.","Kumar, V. R.","Rashed, H.","Yogamani, S.","Vasseur, P.","Honeine, P."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Ahmed","Rida"],"propositions":[],"lastnames":["Sekkat"],"suffixes":[]},{"firstnames":["Yohan"],"propositions":[],"lastnames":["Dupuis"],"suffixes":[]},{"firstnames":["Varun","Ravi"],"propositions":[],"lastnames":["Kumar"],"suffixes":[]},{"firstnames":["Hazem"],"propositions":[],"lastnames":["Rashed"],"suffixes":[]},{"firstnames":["Senthil"],"propositions":[],"lastnames":["Yogamani"],"suffixes":[]},{"firstnames":["Pascal"],"propositions":[],"lastnames":["Vasseur"],"suffixes":[]},{"firstnames":["Paul"],"propositions":[],"lastnames":["Honeine"],"suffixes":[]}],"title":"SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving","booktitle":"Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)","year":"2022","month":"20 October","url_link":"https://arxiv.org/abs/2203.05056","url_paper":"https://arxiv.org/pdf/2203.05056.pdf","keywords":"Fisheye Cameras, Omni-directional Vision, Automated Driving, Synthetic Datasets","abstract":"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. 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 simultaneously in order to sample diverse frames. However, this means that multi-camera algorithms cannot be designed, 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.","bibtex":"@inproceedings{22.iros.SynWoodScape,\n author = \"Ahmed Rida Sekkat and Yohan Dupuis and Varun Ravi Kumar and Hazem Rashed and Senthil Yogamani and Pascal Vasseur and Paul Honeine\",\n title = \"{SynWoodScape}: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving\",\n booktitle = \"Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)\",\n year = \"2022\",\n month = \"20~\" # oct,\n url_link = {https://arxiv.org/abs/2203.05056},\n url_paper = {https://arxiv.org/pdf/2203.05056.pdf},\n keywords = \"Fisheye Cameras, Omni-directional Vision, Automated Driving, Synthetic Datasets\",\n abstract = \"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. 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 simultaneously in order to sample diverse frames. However, this means that multi-camera algorithms cannot be designed, 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.\"\n}\n\n","author_short":["Sekkat, A. R.","Dupuis, Y.","Kumar, V. R.","Rashed, H.","Yogamani, S.","Vasseur, P.","Honeine, P."],"key":"22.iros.SynWoodScape","id":"22.iros.SynWoodScape","bibbaseid":"sekkat-dupuis-kumar-rashed-yogamani-vasseur-honeine-synwoodscapesyntheticsurroundviewfisheyecameradatasetforautonomousdriving-2022","role":"author","urls":{" link":"https://arxiv.org/abs/2203.05056"," paper":"https://arxiv.org/pdf/2203.05056.pdf"},"keyword":["Fisheye Cameras","Omni-directional Vision","Automated Driving","Synthetic Datasets"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"inproceedings","biburl":"http://honeine.fr/paul/biblio_ph.bib","dataSources":["DsERGQxgYm5hGq3CY"],"keywords":["fisheye cameras","omni-directional vision","automated driving","synthetic datasets"],"search_terms":["synwoodscape","synthetic","surround","view","fisheye","camera","dataset","autonomous","driving","sekkat","dupuis","kumar","rashed","yogamani","vasseur","honeine"],"title":"SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving","year":2022}