{"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/da-rnn-semantic-mapping-with-data-associated","title":"DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks","arxiv_id":"1703.03098","date":"2017-03-09","proceeding":null,"authors":["Yu Xiang","Dieter Fox"],"abstract":"3D scene understanding is important for robots to interact with the 3D world\nin a meaningful way. Most previous works on 3D scene understanding focus on\nrecognizing geometrical or semantic properties of the scene independently. In\nthis work, we introduce Data Associated Recurrent Neural Networks (DA-RNNs), a\nnovel framework for joint 3D scene mapping and semantic labeling. DA-RNNs use a\nnew recurrent neural network architecture for semantic labeling on RGB-D\nvideos. The output of the network is integrated with mapping techniques such as\nKinectFusion in order to inject semantic information into the reconstructed 3D\nscene. Experiments conducted on a real world dataset and a synthetic dataset\nwith RGB-D videos demonstrate the ability of our method in semantic 3D scene\nmapping.","url_abs":"http://arxiv.org/abs/1703.03098v2","url_pdf":"http://arxiv.org/pdf/1703.03098v2.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":"da-rnn-semantic-mapping-with-data-associated","repo_url":"https://github.com/hz-ants/DA-RNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"scene-understanding","task_name":"Scene Understanding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}