{"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/extreme-relative-pose-estimation-for-rgb-d","title":"Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion","arxiv_id":"1901.00063","date":"2018-12-31","proceeding":"CVPR 2019 6","authors":["Zhenpei Yang","Jeffrey Z. Pan","Linjie Luo","Xiaowei Zhou","Kristen Grauman","Qi-Xing Huang"],"abstract":"Estimating the relative rigid pose between two RGB-D scans of the same\nunderlying environment is a fundamental problem in computer vision, robotics,\nand computer graphics. Most existing approaches allow only limited maximum\nrelative pose changes since they require considerable overlap between the input\nscans. We introduce a novel deep neural network that extends the scope to\nextreme relative poses, with little or even no overlap between the input scans.\nThe key idea is to infer more complete scene information about the underlying\nenvironment and match on the completed scans. In particular, instead of only\nperforming scene completion from each individual scan, our approach alternates\nbetween relative pose estimation and scene completion. This allows us to\nperform scene completion by utilizing information from both input scans at late\niterations, resulting in better results for both scene completion and relative\npose estimation. Experimental results on benchmark datasets show that our\napproach leads to considerable improvements over state-of-the-art approaches\nfor relative pose estimation. In particular, our approach provides encouraging\nrelative pose estimates even between non-overlapping scans.","url_abs":"http://arxiv.org/abs/1901.00063v2","url_pdf":"http://arxiv.org/pdf/1901.00063v2.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":"extreme-relative-pose-estimation-for-rgb-d","repo_url":"https://github.com/zhenpeiyang/RelativePose","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.00063","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}