{"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/how-to-train-your-event-camera-neural-network","title":"Reducing the Sim-to-Real Gap for Event Cameras","arxiv_id":"2003.09078","date":"2020-03-20","proceeding":"ECCV 2020 8","authors":["Timo Stoffregen","Cedric Scheerlinck","Davide Scaramuzza","Tom Drummond","Nick Barnes","Lindsay Kleeman","Robert Mahony"],"abstract":"Event cameras are paradigm-shifting novel sensors that report asynchronous, per-pixel brightness changes called 'events' with unparalleled low latency. This makes them ideal for high speed, high dynamic range scenes where conventional cameras would fail. Recent work has demonstrated impressive results using Convolutional Neural Networks (CNNs) for video reconstruction and optic flow with events. We present strategies for improving training data for event based CNNs that result in 20-40% boost in performance of existing state-of-the-art (SOTA) video reconstruction networks retrained with our method, and up to 15% for optic flow networks. A challenge in evaluating event based video reconstruction is lack of quality ground truth images in existing datasets. To address this, we present a new High Quality Frames (HQF) dataset, containing events and ground truth frames from a DAVIS240C that are well-exposed and minimally motion-blurred. We evaluate our method on HQF + several existing major event camera datasets.","url_abs":"https://arxiv.org/abs/2003.09078v5","url_pdf":"https://arxiv.org/pdf/2003.09078v5.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":"how-to-train-your-event-camera-neural-network","repo_url":"https://github.com/TimoStoff/event_cnn_minimal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"event-based-video-reconstruction","task_name":"Event-Based Video Reconstruction"},{"task_slug":"video-reconstruction","task_name":"Video Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-reconstruction-on-event-camera-dataset","task":"Video Reconstruction","dataset":"Event-Camera Dataset","model":"E2VID+","rank_in_archive_order":3,"of":4,"metrics":{"LPIPS":"0.236","Mean Squared Error":"0.070"},"uses_additional_data":false},{"leaderboard":"/sota/video-reconstruction-on-mvsec","task":"Video Reconstruction","dataset":"MVSEC","model":"E2VID+","rank_in_archive_order":3,"of":4,"metrics":{"LPIPS":"0.514","Mean Squared Error":"0.132"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}