{"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/practical-optimal-registration-of-terrestrial","title":"Practical optimal registration of terrestrial LiDAR scan pairs","arxiv_id":"1811.09962","date":"2018-11-25","proceeding":null,"authors":["Zhipeng Cai","Tat-Jun Chin","Alvaro Parra Bustos","Konrad Schindler"],"abstract":"Point cloud registration is a fundamental problem in 3D scanning. In this\npaper, we address the frequent special case of registering terrestrial LiDAR\nscans (or, more generally, levelled point clouds). Many current solutions still\nrely on the Iterative Closest Point (ICP) method or other heuristic procedures,\nwhich require good initializations to succeed and/or provide no guarantees of\nsuccess. On the other hand, exact or optimal registration algorithms can\ncompute the best possible solution without requiring initializations; however,\nthey are currently too slow to be practical in realistic applications.\n  Existing optimal approaches ignore the fact that in routine use the relative\nrotations between scans are constrained to the azimuth, via the built-in level\ncompensation in LiDAR scanners. We propose a novel, optimal and computationally\nefficient registration method for this 4DOF scenario. Our approach operates on\ncandidate 3D keypoint correspondences, and contains two main steps: (1) a\ndeterministic selection scheme that significantly reduces the candidate\ncorrespondence set in a way that is guaranteed to preserve the optimal\nsolution; and (2) a fast branch-and-bound (BnB) algorithm with a novel\npolynomial-time subroutine for 1D rotation search, that quickly finds the\noptimal alignment for the reduced set. We demonstrate the practicality of our\nmethod on realistic point clouds from multiple LiDAR surveys.","url_abs":"http://arxiv.org/abs/1811.09962v3","url_pdf":"http://arxiv.org/pdf/1811.09962v3.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":"practical-optimal-registration-of-terrestrial","repo_url":"https://github.com/ZhipengCai/Demo---Practical-optimal-registration-of-terrestrial-LiDAR-scan-pairs","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"point-cloud-registration","task_name":"Point Cloud Registration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.09962","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}