{"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/referit3d-neural-listeners-for-fine-grained","title":"ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes","arxiv_id":null,"date":"2020-08-01","proceeding":"ECCV 2020 8","authors":["Panos Achlioptas","Ahmed Abdelreheem","Fei Xia","Mohamed Elhoseiny","Leonidas Guibas"],"abstract":"In this work we study the problem of using referential language to identify common objects in real-world 3D scenes. We focus on a challenging setup where the referred object belongs to a extit{fine-grained} object class and the underlying scene contains extit{multiple} object instances of that class. Due to the scarcity and unsuitability of existent 3D-oriented linguistic resources for this task, we first develop two large-scale and complementary visio-linguistic datasets: i) extbf{ extit{Sr3D}}, which contains 83.5K template-based utterances leveraging extit{spatial relations} with other fine-grained object classes to localize a referred object in a given scene, and ii) extbf{ extit{Nr3D}} which contains 41.5K extit{natural, free-form}, utterances collected by deploying a 2-player object reference game in 3D scenes. Using utterances of either datasets, human listeners can recognize the referred object with high ($>$86\\%, 92\\% resp.) accuracy. By tapping on this data, we develop novel neural listeners that can comprehend object-centric natural language and identify the referred object extit{directly} in a 3D scene. Our key technical contribution is designing an approach for combining linguistic and geometric information (in the form of 3D point-clouds) and creating multi-modal (3D) neural listeners. We also show that architectures which promote object-to-object communication via graph neural networks outperform less context-aware alternatives, and that language-assisted 3D object identification outperforms language-agnostic object classifiers.","url_abs":"https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1501_ECCV_2020_paper.php","url_pdf":"https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460409.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":"referit3d-neural-listeners-for-fine-grained","repo_url":"https://github.com/referit3d/referit3d","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"}],"methods":[],"datasets_introduced":[{"slug":"referit3d","name":"ReferIt3D","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}