{"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/parse-geometry-from-a-line-monocular-depth","title":"Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation","arxiv_id":"1611.02174","date":"2016-10-17","proceeding":null,"authors":["Yiyi Liao","Lichao Huang","Yue Wang","Sarath Kodagoda","Yinan Yu","Yong liu"],"abstract":"Many standard robotic platforms are equipped with at least a fixed 2D laser\nrange finder and a monocular camera. Although those platforms do not have\nsensors for 3D depth sensing capability, knowledge of depth is an essential\npart in many robotics activities. Therefore, recently, there is an increasing\ninterest in depth estimation using monocular images. As this task is inherently\nambiguous, the data-driven estimated depth might be unreliable in robotics\napplications. In this paper, we have attempted to improve the precision of\nmonocular depth estimation by introducing 2D planar observation from the\nremaining laser range finder without extra cost. Specifically, we construct a\ndense reference map from the sparse laser range data, redefining the depth\nestimation task as estimating the distance between the real and the reference\ndepth. To solve the problem, we construct a novel residual of residual neural\nnetwork, and tightly combine the classification and regression losses for\ncontinuous depth estimation. Experimental results suggest that our method\nachieves considerable promotion compared to the state-of-the-art methods on\nboth NYUD2 and KITTI, validating the effectiveness of our method on leveraging\nthe additional sensory information. We further demonstrate the potential usage\nof our method in obstacle avoidance where our methodology provides\ncomprehensive depth information compared to the solution using monocular camera\nor 2D laser range finder alone.","url_abs":"http://arxiv.org/abs/1611.02174v1","url_pdf":"http://arxiv.org/pdf/1611.02174v1.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":"parse-geometry-from-a-line-monocular-depth","repo_url":"https://github.com/EbadSyed/spadRGBD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"parse-geometry-from-a-line-monocular-depth","repo_url":"https://github.com/LeonSun0101/CD-SD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"parse-geometry-from-a-line-monocular-depth","repo_url":"https://github.com/fangchangma/sparse-to-dense.pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"parse-geometry-from-a-line-monocular-depth","repo_url":"https://github.com/katieluo88/280finalproj_nyudepth","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"depth-completion","task_name":"Depth Completion"},{"task_slug":"depth-estimation","task_name":"Depth Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1611.02174","atlas_url":"https://app.syntology.ai/?focus=1611.02174","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}