{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T06:06:16Z","timestamp":1784268376011,"version":"3.55.0"},"reference-count":45,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,10,24]],"date-time":"2020-10-24T00:00:00Z","timestamp":1603497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,10,24]],"date-time":"2020-10-24T00:00:00Z","timestamp":1603497600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,10,24]],"date-time":"2020-10-24T00:00:00Z","timestamp":1603497600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,10,24]]},"DOI":"10.1109\/iros45743.2020.9340998","type":"proceedings-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T14:49:56Z","timestamp":1615819796000},"page":"10233-10240","source":"Crossref","is-referenced-by-count":70,"title":["Depth Estimation from Monocular Images and Sparse Radar Data"],"prefix":"10.1109","author":[{"given":"Juan-Ting","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dengxin","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luc Van","family":"Gool","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Automotive radar dataset for deep learning based 3d object detection","author":"meyer","year":"2019","journal-title":"European Radar Conference (EuRAD)"},{"key":"ref38","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-34879-3_27","article-title":"So-net: Joint semantic segmentation and obstacle detection using deep fusion of monocular camera and radar","author":"john","year":"2019","journal-title":"Pacific-Rim Sympo-sium on Image and Video Technology"},{"key":"ref33","article-title":"Plug-and-play: Improve depth estimation via sparse data propagation","author":"wang","year":"2018","journal-title":"International Conference on Robotics and Automation (ICRA)"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793637"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2018.00017"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.699"},{"key":"ref37","doi-asserted-by":"crossref","DOI":"10.1109\/SDF.2019.8916629","article-title":"A deep learning-based radar and camera sensor fusion architecture for object detection","author":"nobis","year":"2019","journal-title":"Sensor Data Fusion Trends Solutions Applications (SDF)"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794312"},{"key":"ref35","article-title":"Depth estimation via affinity learned with convolutional spatial propagation network","author":"cheng","year":"2018","journal-title":"European Conference on Computer Vision (ECCV)"},{"key":"ref34","article-title":"Adversarial examples in the physical world","author":"kurakin","year":"2016"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.23919\/ICIF.2018.8455344"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1023\/A:1014573219977"},{"key":"ref13","article-title":"Deepstereo: Learning to predict new views from the world’s imagery","author":"flynn","year":"2016","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"ref14","article-title":"Make3d: Learning 3d scene structure from a single still image","author":"saxena","year":"2008","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.19"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-006-0031-y"},{"key":"ref17","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.238"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.32"},{"key":"ref27","article-title":"Unsupervised cnn for single view depth estimation: Geometry to the rescue","author":"garg","year":"2016","journal-title":"European Conference on Computer Vision (ECCV)"},{"key":"ref3","article-title":"Unsupervised cnn for single view depth estimation: Geometry to the rescue","author":"garg","year":"2016","journal-title":"European Conference on Computer Vision (ECCV)"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00343"},{"key":"ref29","article-title":"Digging into self-supervised monocular depth prediction","author":"godard","year":"2019","journal-title":"IEEE International Conference on Computer Vision (ICCV)"},{"key":"ref5","article-title":"Don’t forget the past: Recurrent depth estimation from monocular video","author":"vaishakh","year":"2020","journal-title":"IROS"},{"key":"ref8","article-title":"Deep continuous fusion for multi-sensor 3d object detection","author":"liang","year":"2018","journal-title":"European Conference on Computer Vision (ECCV)"},{"key":"ref7","article-title":"Sparse-to-dense: Depth prediction from sparse depth samples and a single image","author":"mal","year":"2018","journal-title":"IEEE International Conference on Robotics and Automation (ICRA)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00214"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00752"},{"key":"ref1","article-title":"Indoor segmentation and support inference from rgbd images","author":"nathan silberman","year":"2012","journal-title":"European Conference on Computer Vision (ECCV)"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00446"},{"key":"ref22","article-title":"Microsoft COCO: Common objects in context","author":"lin","year":"2014","journal-title":"European Conference on Computer Vision (ECCV)"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref42","article-title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","author":"kendall","year":"2018","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.365"},{"key":"ref41","article-title":"Semantic object prediction and spatial sound super-resolution with binaural sounds","author":"vasudevan","year":"2020","journal-title":"ECCV"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.304"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.700"},{"key":"ref43","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref25","article-title":"Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks","author":"xie","year":"2016","journal-title":"European Conference on Computer Vision (ECCV)"}],"event":{"name":"2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","location":"Las Vegas, NV, USA","start":{"date-parts":[[2020,10,24]]},"end":{"date-parts":[[2021,1,24]]}},"container-title":["2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9340668\/9340635\/09340998.pdf?arnumber=9340998","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T21:57:22Z","timestamp":1656453442000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9340998\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,24]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/iros45743.2020.9340998","relation":{},"subject":[],"published":{"date-parts":[[2020,10,24]]}}}