{"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-fast-method-for-computing-principal","title":"A Fast Method For Computing Principal Curvatures From Range Images","arxiv_id":"1707.00385","date":"2017-07-03","proceeding":null,"authors":["Andrew Spek","Wai Ho Li","Tom Drummond"],"abstract":"Estimation of surface curvature from range data is important for a range of\ntasks in computer vision and robotics, object segmentation, object recognition\nand robotic grasping estimation. This work presents a fast method of robustly\ncomputing accurate metric principal curvature values from noisy point clouds\nwhich was implemented on GPU. In contrast to existing readily available\nsolutions which first differentiate the surface to estimate surface normals and\nthen differentiate these to obtain curvature, amplifying noise, our method\niteratively fits parabolic quadric surface patches to the data. Additionally\nprevious methods with a similar formulation use less robust techniques less\napplicable to a high noise sensor. We demonstrate that our method is fast and\nprovides better curvature estimates than existing techniques. In particular we\ncompare our method to several alternatives to demonstrate the improvement.","url_abs":"http://arxiv.org/abs/1707.00385v2","url_pdf":"http://arxiv.org/pdf/1707.00385v2.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-fast-method-for-computing-principal","repo_url":"https://github.com/aspek1/QuadricCurvature","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"robotic-grasping","task_name":"Robotic Grasping"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}