{"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/associatively-segmenting-instances-and","title":"Associatively Segmenting Instances and Semantics in Point Clouds","arxiv_id":"1902.09852","date":"2019-02-26","proceeding":"CVPR 2019 6","authors":["Xinlong Wang","Shu Liu","Xiaoyong Shen","Chunhua Shen","Jiaya Jia"],"abstract":"A 3D point cloud describes the real scene precisely and intuitively.To date\nhow to segment diversified elements in such an informative 3D scene is rarely\ndiscussed. In this paper, we first introduce a simple and flexible framework to\nsegment instances and semantics in point clouds simultaneously. Then, we\npropose two approaches which make the two tasks take advantage of each other,\nleading to a win-win situation. Specifically, we make instance segmentation\nbenefit from semantic segmentation through learning semantic-aware point-level\ninstance embedding. Meanwhile, semantic features of the points belonging to the\nsame instance are fused together to make more accurate per-point semantic\npredictions. Our method largely outperforms the state-of-the-art method in 3D\ninstance segmentation along with a significant improvement in 3D semantic\nsegmentation. Code has been made available at:\nhttps://github.com/WXinlong/ASIS.","url_abs":"http://arxiv.org/abs/1902.09852v2","url_pdf":"http://arxiv.org/pdf/1902.09852v2.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":"associatively-segmenting-instances-and","repo_url":"https://github.com/WXinlong/ASIS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"associatively-segmenting-instances-and","repo_url":"https://github.com/LebronGG/ASIS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"associatively-segmenting-instances-and","repo_url":"https://github.com/tuananh1007/Associatively-Segmenting-Instances-and-Semantics-in-Point-Clouds","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"3d-instance-segmentation-1","task_name":"3D Instance Segmentation"},{"task_slug":"3d-semantic-segmentation","task_name":"3D Semantic Segmentation"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-instance-segmentation-on-s3dis","task":"3D Instance Segmentation","dataset":"S3DIS","model":"ASIS","rank_in_archive_order":19,"of":21,"metrics":{"mPrec":"63.6","mRec":"47.5"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-s3dis","task":"Semantic Segmentation","dataset":"S3DIS","model":"ASIS","rank_in_archive_order":45,"of":54,"metrics":{"Mean IoU":"59.3","Number of params":"N/A"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.09852","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}