{"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-region-growing-approach-for-automatic","title":"A region-growing approach for automatic outcrop fracture extraction from a three-dimensional point cloud","arxiv_id":"1707.03266","date":"2017-06-27","proceeding":null,"authors":["Xin Wang","Lejun Zou","Xiaohua Shen","Yupeng Ren","Yi Qin"],"abstract":"Conventional manual surveys of rock mass fractures usually require large\namounts of time and labor; yet, they provide a relatively small set of data\nthat cannot be considered representative of the study region. Terrestrial laser\nscanners are increasingly used for fracture surveys because they can\nefficiently acquire large area, high-resolution, three-dimensional (3D) point\nclouds from outcrops. However, extracting fractures and other planar surfaces\nfrom 3D outcrop point clouds is still a challenging task. No method has been\nreported that can be used to automatically extract the full extent of every\nindividual fracture from a 3D outcrop point cloud. In this study, we propose a\nmethod using a region-growing approach to address this problem; the method also\nestimates the orientation of each fracture. In this method, criteria based on\nthe local surface normal and curvature of the point cloud are used to initiate\nand control the growth of the fracture region. In tests using outcrop point\ncloud data, the proposed method identified and extracted the full extent of\nindividual fractures with high accuracy. Compared with manually acquired field\nsurvey data, our method obtained better-quality fracture data, thereby\ndemonstrating the high potential utility of the proposed method.","url_abs":"http://arxiv.org/abs/1707.03266v1","url_pdf":"http://arxiv.org/pdf/1707.03266v1.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-region-growing-approach-for-automatic","repo_url":"https://github.com/EricAlex/structrock","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}