{"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/semantic-classification-of-3d-point-clouds","title":"Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods","arxiv_id":"1808.00495","date":"2018-08-01","proceeding":null,"authors":["Hugues Thomas","Jean-Emmanuel Deschaud","Beatriz Marcotegui","François Goulette","Yann Le Gall"],"abstract":"This paper introduces a new definition of multiscale neighborhoods in 3D\npoint clouds. This definition, based on spherical neighborhoods and\nproportional subsampling, allows the computation of features with a consistent\ngeometrical meaning, which is not the case when using k-nearest neighbors. With\nan appropriate learning strategy, the proposed features can be used in a random\nforest to classify 3D points. In this semantic classification task, we show\nthat our multiscale features outperform state-of-the-art features using the\nsame experimental conditions. Furthermore, their classification power competes\nwith more elaborate classification approaches including Deep Learning methods.","url_abs":"http://arxiv.org/abs/1808.00495v1","url_pdf":"http://arxiv.org/pdf/1808.00495v1.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":[],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-semantic3d","task":"Semantic Segmentation","dataset":"Semantic3D","model":"RF_MSSF","rank_in_archive_order":12,"of":17,"metrics":{"mIoU":"62.7%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.00495","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}