{"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-minimal-closed-form-solution-for-multi","title":"A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines","arxiv_id":"1807.09970","date":"2018-07-26","proceeding":"ECCV 2018 9","authors":["Pedro Miraldo","Tiago Dias","Srikumar Ramalingam"],"abstract":"We propose a minimal solution for pose estimation using both points and lines\nfor a multi-perspective camera. In this paper, we treat the multi-perspective\ncamera as a collection of rigidly attached perspective cameras. These type of\nimaging devices are useful for several computer vision applications that\nrequire a large coverage such as surveillance, self-driving cars, and\nmotion-capture studios. While prior methods have considered the cases using\nsolely points or lines, the hybrid case involving both points and lines has not\nbeen solved for multi-perspective cameras. We present the solutions for two\ncases. In the first case, we are given 2D to 3D correspondences for two points\nand one line. In the later case, we are given 2D to 3D correspondences for one\npoint and two lines. We show that the solution for the case of two points and\none line can be formulated as a fourth degree equation. This is interesting\nbecause we can get a closed-form solution and thereby achieve high\ncomputational efficiency. The later case involving two lines and one point can\nbe mapped to an eighth degree equation. We show simulations and real\nexperiments to demonstrate the advantages and benefits over existing methods.","url_abs":"http://arxiv.org/abs/1807.09970v1","url_pdf":"http://arxiv.org/pdf/1807.09970v1.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-minimal-closed-form-solution-for-multi","repo_url":"https://github.com/pmiraldo/MinimalMultiPerspectivePose","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"form","task_name":"Form"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"self-driving-cars","task_name":"Self-Driving Cars"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.09970","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}