{"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/a-unifying-contrast-maximization-framework","title":"A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation","arxiv_id":"1804.01306","date":"2018-04-04","proceeding":"CVPR 2018 6","authors":["Guillermo Gallego","Henri Rebecq","Davide Scaramuzza"],"abstract":"We present a unifying framework to solve several computer vision problems\nwith event cameras: motion, depth and optical flow estimation. The main idea of\nour framework is to find the point trajectories on the image plane that are\nbest aligned with the event data by maximizing an objective function: the\ncontrast of an image of warped events. Our method implicitly handles data\nassociation between the events, and therefore, does not rely on additional\nappearance information about the scene. In addition to accurately recovering\nthe motion parameters of the problem, our framework produces motion-corrected\nedge-like images with high dynamic range that can be used for further scene\nanalysis. The proposed method is not only simple, but more importantly, it is,\nto the best of our knowledge, the first method that can be successfully applied\nto such a diverse set of important vision tasks with event cameras.","url_abs":"http://arxiv.org/abs/1804.01306v1","url_pdf":"http://arxiv.org/pdf/1804.01306v1.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":"a-unifying-contrast-maximization-framework","repo_url":"https://github.com/neuromorphicsystems/event_warping","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"a-unifying-contrast-maximization-framework","repo_url":"https://github.com/tub-rip/dvs_global_flow_skeleton","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.01306","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}