{"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/text2loc-3d-point-cloud-localization-from","title":"Text2Loc: 3D Point Cloud Localization from Natural Language","arxiv_id":"2311.15977","date":"2023-11-27","proceeding":"CVPR 2024 1","authors":["Yan Xia","Letian Shi","Zifeng Ding","João F. Henriques","Daniel Cremers"],"abstract":"We tackle the problem of 3D point cloud localization based on a few natural linguistic descriptions and introduce a novel neural network, Text2Loc, that fully interprets the semantic relationship between points and text. Text2Loc follows a coarse-to-fine localization pipeline: text-submap global place recognition, followed by fine localization. In global place recognition, relational dynamics among each textual hint are captured in a hierarchical transformer with max-pooling (HTM), whereas a balance between positive and negative pairs is maintained using text-submap contrastive learning. Moreover, we propose a novel matching-free fine localization method to further refine the location predictions, which completely removes the need for complicated text-instance matching and is lighter, faster, and more accurate than previous methods. Extensive experiments show that Text2Loc improves the localization accuracy by up to $2\\times$ over the state-of-the-art on the KITTI360Pose dataset. Our project page is publicly available at \\url{https://yan-xia.github.io/projects/text2loc/}.","url_abs":"https://arxiv.org/abs/2311.15977v2","url_pdf":"https://arxiv.org/pdf/2311.15977v2.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":"text2loc-3d-point-cloud-localization-from","repo_url":"https://github.com/kevin301342/cmmloc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"visual-place-recognition","task_name":"Visual Place Recognition"}],"methods":[{"method_slug":"hint","method_name":"HINT"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-place-recognition-on-kitti360pose","task":"Visual Place Recognition","dataset":"KITTI360pose","model":"Text2Loc","rank_in_archive_order":2,"of":5,"metrics":{"Localization Recall@1 ":"0.37"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.15977","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}