{"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/estimating-depth-from-rgb-and-sparse-sensing","title":"Estimating Depth from RGB and Sparse Sensing","arxiv_id":"1804.02771","date":"2018-04-08","proceeding":"ECCV 2018 9","authors":["Zhao Chen","Vijay Badrinarayanan","Gilad Drozdov","Andrew Rabinovich"],"abstract":"We present a deep model that can accurately produce dense depth maps given an\nRGB image with known depth at a very sparse set of pixels. The model works\nsimultaneously for both indoor/outdoor scenes and produces state-of-the-art\ndense depth maps at nearly real-time speeds on both the NYUv2 and KITTI\ndatasets. We surpass the state-of-the-art for monocular depth estimation even\nwith depth values for only 1 out of every ~10000 image pixels, and we\noutperform other sparse-to-dense depth methods at all sparsity levels. With\ndepth values for 1/256 of the image pixels, we achieve a mean absolute error of\nless than 1% of actual depth on indoor scenes, comparable to the performance of\nconsumer-grade depth sensor hardware. Our experiments demonstrate that it would\nindeed be possible to efficiently transform sparse depth measurements obtained\nusing e.g. lower-power depth sensors or SLAM systems into high-quality dense\ndepth maps.","url_abs":"http://arxiv.org/abs/1804.02771v2","url_pdf":"http://arxiv.org/pdf/1804.02771v2.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":"estimating-depth-from-rgb-and-sparse-sensing","repo_url":"https://github.com/kvmanohar22/sparse_depth_sensing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"estimating-depth-from-rgb-and-sparse-sensing","repo_url":"https://github.com/lakshjaisinghani/Estimating-Depth-from-RGB-and-Sparse-Sensing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1804.02771","atlas_url":"https://app.syntology.ai/?focus=1804.02771","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}