{"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-photometrically-calibrated-benchmark-for","title":"A Photometrically Calibrated Benchmark For Monocular Visual Odometry","arxiv_id":"1607.02555","date":"2016-07-09","proceeding":null,"authors":["Jakob Engel","Vladyslav Usenko","Daniel Cremers"],"abstract":"We present a dataset for evaluating the tracking accuracy of monocular visual\nodometry and SLAM methods. It contains 50 real-world sequences comprising more\nthan 100 minutes of video, recorded across dozens of different environments --\nranging from narrow indoor corridors to wide outdoor scenes. All sequences\ncontain mostly exploring camera motion, starting and ending at the same\nposition. This allows to evaluate tracking accuracy via the accumulated drift\nfrom start to end, without requiring ground truth for the full sequence. In\ncontrast to existing datasets, all sequences are photometrically calibrated. We\nprovide exposure times for each frame as reported by the sensor, the camera\nresponse function, and dense lens attenuation factors. We also propose a novel,\nsimple approach to non-parametric vignette calibration, which requires minimal\nset-up and is easy to reproduce. Finally, we thoroughly evaluate two existing\nmethods (ORB-SLAM and DSO) on the dataset, including an analysis of the effect\nof image resolution, camera field of view, and the camera motion direction.","url_abs":"http://arxiv.org/abs/1607.02555v2","url_pdf":"http://arxiv.org/pdf/1607.02555v2.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":[],"tasks":[{"task_slug":"monocular-visual-odometry","task_name":"Monocular Visual Odometry"},{"task_slug":"visual-odometry","task_name":"Visual Odometry"}],"methods":[],"datasets_introduced":[{"slug":"tum-monovo","name":"TUM monoVO","full_name":"TUM monoVO"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1607.02555","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}