{"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/classification-of-point-cloud-scenes-with","title":"Classification of Point Cloud Scenes with Multiscale Voxel Deep Network","arxiv_id":"1804.03583","date":"2018-04-10","proceeding":null,"authors":["Xavier Roynard","Jean-Emmanuel Deschaud","François Goulette"],"abstract":"In this article we describe a new convolutional neural network (CNN) to\nclassify 3D point clouds of urban or indoor scenes. Solutions are given to the\nproblems encountered working on scene point clouds, and a network is described\nthat allows for point classification using only the position of points in a\nmulti-scale neighborhood.\n  On the reduced-8 Semantic3D benchmark [Hackel et al., 2017], this network,\nranked second, beats the state of the art of point classification methods\n(those not using a regularization step).","url_abs":"http://arxiv.org/abs/1804.03583v1","url_pdf":"http://arxiv.org/pdf/1804.03583v1.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":"classification-of-point-cloud-scenes-with","repo_url":"https://github.com/xroynard/ms_deepvoxscene","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":null,"task_name":"Position"},{"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":"MSDeepVoxNet","rank_in_archive_order":11,"of":17,"metrics":{"mIoU":"65.3%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.03583","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}