{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-event-camera-dataset-and-simulator-event","title":"The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM","arxiv_id":"1610.08336","date":"2016-10-26","proceeding":null,"authors":["Elias Mueggler","Henri Rebecq","Guillermo Gallego","Tobi Delbruck","Davide Scaramuzza"],"abstract":"New vision sensors, such as the Dynamic and Active-pixel Vision sensor\n(DAVIS), incorporate a conventional global-shutter camera and an event-based\nsensor in the same pixel array. These sensors have great potential for\nhigh-speed robotics and computer vision because they allow us to combine the\nbenefits of conventional cameras with those of event-based sensors: low\nlatency, high temporal resolution, and very high dynamic range. However, new\nalgorithms are required to exploit the sensor characteristics and cope with its\nunconventional output, which consists of a stream of asynchronous brightness\nchanges (called \"events\") and synchronous grayscale frames. For this purpose,\nwe present and release a collection of datasets captured with a DAVIS in a\nvariety of synthetic and real environments, which we hope will motivate\nresearch on new algorithms for high-speed and high-dynamic-range robotics and\ncomputer-vision applications. In addition to global-shutter intensity images\nand asynchronous events, we provide inertial measurements and ground-truth\ncamera poses from a motion-capture system. The latter allows comparing the pose\naccuracy of ego-motion estimation algorithms quantitatively. All the data are\nreleased both as standard text files and binary files (i.e., rosbag). This\npaper provides an overview of the available data and describes a simulator that\nwe release open-source to create synthetic event-camera data.","url_abs":"http://arxiv.org/abs/1610.08336v4","url_pdf":"http://arxiv.org/pdf/1610.08336v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-event-camera-dataset-and-simulator-event","repo_url":"https://github.com/uzh-rpg/rpg_davis_simulator","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"the-event-camera-dataset-and-simulator-event","repo_url":"https://github.com/event-driven-robotics/bimvee","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"motion-estimation","task_name":"Motion Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"visual-odometry","task_name":"Visual Odometry"}],"methods":[],"datasets_introduced":[{"slug":"event-camera-dataset","name":"Event-Camera Dataset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1610.08336","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1610.08336"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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