{"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/direct-sparse-odometry","title":"Direct Sparse Odometry","arxiv_id":"1607.02565","date":"2016-07-09","proceeding":null,"authors":["Jakob Engel","Vladlen Koltun","Daniel Cremers"],"abstract":"We propose a novel direct sparse visual odometry formulation. It combines a\nfully direct probabilistic model (minimizing a photometric error) with\nconsistent, joint optimization of all model parameters, including geometry --\nrepresented as inverse depth in a reference frame -- and camera motion. This is\nachieved in real time by omitting the smoothness prior used in other direct\nmethods and instead sampling pixels evenly throughout the images. Since our\nmethod does not depend on keypoint detectors or descriptors, it can naturally\nsample pixels from across all image regions that have intensity gradient,\nincluding edges or smooth intensity variations on mostly white walls. The\nproposed model integrates a full photometric calibration, accounting for\nexposure time, lens vignetting, and non-linear response functions. We\nthoroughly evaluate our method on three different datasets comprising several\nhours of video. The experiments show that the presented approach significantly\noutperforms state-of-the-art direct and indirect methods in a variety of\nreal-world settings, both in terms of tracking accuracy and robustness.","url_abs":"http://arxiv.org/abs/1607.02565v2","url_pdf":"http://arxiv.org/pdf/1607.02565v2.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":"direct-sparse-odometry","repo_url":"https://github.com/muskie82/CNN-DSO","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"direct-sparse-odometry","repo_url":"https://github.com/JakobEngel/dso","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"visual-odometry","task_name":"Visual Odometry"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1607.02565","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}