{"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/cnn-slam-real-time-dense-monocular-slam-with","title":"CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction","arxiv_id":"1704.03489","date":"2017-04-11","proceeding":"CVPR 2017 7","authors":["Keisuke Tateno","Federico Tombari","Iro Laina","Nassir Navab"],"abstract":"Given the recent advances in depth prediction from Convolutional Neural\nNetworks (CNNs), this paper investigates how predicted depth maps from a deep\nneural network can be deployed for accurate and dense monocular reconstruction.\nWe propose a method where CNN-predicted dense depth maps are naturally fused\ntogether with depth measurements obtained from direct monocular SLAM. Our\nfusion scheme privileges depth prediction in image locations where monocular\nSLAM approaches tend to fail, e.g. along low-textured regions, and vice-versa.\nWe demonstrate the use of depth prediction for estimating the absolute scale of\nthe reconstruction, hence overcoming one of the major limitations of monocular\nSLAM. Finally, we propose a framework to efficiently fuse semantic labels,\nobtained from a single frame, with dense SLAM, yielding semantically coherent\nscene reconstruction from a single view. Evaluation results on two benchmark\ndatasets show the robustness and accuracy of our approach.","url_abs":"http://arxiv.org/abs/1704.03489v1","url_pdf":"http://arxiv.org/pdf/1704.03489v1.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":"cnn-slam-real-time-dense-monocular-slam-with","repo_url":"https://github.com/iitmcvg/CNN_SLAM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"depth-prediction","task_name":"Depth Prediction"},{"task_slug":"monocular-reconstruction","task_name":"Monocular Reconstruction"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1704.03489","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.03489"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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