{"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/3d-bevis-birds-eye-view-instance-segmentation","title":"3D-BEVIS: Bird's-Eye-View Instance Segmentation","arxiv_id":"1904.02199","date":"2019-04-03","proceeding":null,"authors":["Cathrin Elich","Francis Engelmann","Theodora Kontogianni","Bastian Leibe"],"abstract":"Recent deep learning models achieve impressive results on 3D scene analysis tasks by operating directly on unstructured point clouds. A lot of progress was made in the field of object classification and semantic segmentation. However, the task of instance segmentation is less explored. In this work, we present 3D-BEVIS, a deep learning framework for 3D semantic instance segmentation on point clouds. Following the idea of previous proposal-free instance segmentation approaches, our model learns a feature embedding and groups the obtained feature space into semantic instances. Current point-based methods scale linearly with the number of points by processing local sub-parts of a scene individually. However, to perform instance segmentation by clustering, globally consistent features are required. Therefore, we propose to combine local point geometry with global context information from an intermediate bird's-eye view representation.","url_abs":"https://arxiv.org/abs/1904.02199v3","url_pdf":"https://arxiv.org/pdf/1904.02199v3.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":"3d-instance-segmentation-1","task_name":"3D Instance Segmentation"},{"task_slug":"3d-semantic-instance-segmentation","task_name":"3D Semantic Instance Segmentation"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"},{"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-semantic-instance-segmentation-on-1","task":"3D Semantic Instance Segmentation","dataset":"ScanNetV2","model":"3D-BEVIS","rank_in_archive_order":4,"of":5,"metrics":{"mAP@0.50":"24.8"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.02199","atlas_url":"https://app.syntology.ai/?focus=1904.02199","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}