{"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/fast-cylinder-and-plane-extraction-from-depth","title":"Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry","arxiv_id":"1803.02380","date":"2018-03-06","proceeding":null,"authors":["Pedro F. Proença","Yang Gao"],"abstract":"This paper presents CAPE, a method to extract planes and cylinder segments\nfrom organized point clouds, which processes 640x480 depth images on a single\nCPU core at an average of 300 Hz, by operating on a grid of planar cells.\nWhile, compared to state-of-the-art plane extraction, the latency of CAPE is\nmore consistent and 4-10 times faster, depending on the scene, we also\ndemonstrate empirically that applying CAPE to visual odometry can improve\ntrajectory estimation on scenes made of cylindrical surfaces (e.g. tunnels),\nwhereas using a plane extraction approach that is not curve-aware deteriorates\nperformance on these scenes. To use these geometric primitives in visual\nodometry, we propose extending a probabilistic RGB-D odometry framework based\non points, lines and planes to cylinder primitives. Following this framework,\nCAPE runs on fused depth maps and the parameters of cylinders are modelled\nprobabilistically to account for uncertainty and weight accordingly the pose\noptimization residuals.","url_abs":"http://arxiv.org/abs/1803.02380v3","url_pdf":"http://arxiv.org/pdf/1803.02380v3.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":"fast-cylinder-and-plane-extraction-from-depth","repo_url":"https://github.com/pedropro/CAPE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"fast-cylinder-and-plane-extraction-from-depth","repo_url":"https://github.com/HuchieWuchie/CAPE-python","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"visual-odometry","task_name":"Visual Odometry"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.02380","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}