{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T22:25:19Z","timestamp":1726007119741},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030110086"},{"type":"electronic","value":"9783030110093"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-11009-3_14","type":"book-chapter","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T01:24:44Z","timestamp":1548293084000},"page":"240-255","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploiting Single Image Depth Prediction for Mono-stixel Estimation"],"prefix":"10.1007","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-3527-9323","authenticated-orcid":false,"given":"Fabian","family":"Brickwedde","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1320-4275","authenticated-orcid":false,"given":"Steffen","family":"Abraham","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6932-0606","authenticated-orcid":false,"given":"Rudolf","family":"Mester","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,1,23]]},"reference":[{"key":"14_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/978-3-642-03798-6_6","volume-title":"Pattern Recognition","author":"H Badino","year":"2009","unstructured":"Badino, H., Franke, U., Pfeiffer, D.: The stixel world - a compact medium level representation of the 3D-world. In: Denzler, J., Notni, G., S\u00fc\u00dfe, H. (eds.) DAGM 2009. LNCS, vol. 5748, pp. 51\u201360. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-03798-6_6"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Brickwedde, F., Abraham, S., Mester, R.: Mono-Stixels: monocular depth reconstruction of dynamic street scenes. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8460490"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"14_CR4","unstructured":"Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: Advances in Neural Information Processing Systems, pp. 2366\u20132374 (2014)"},{"key":"14_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1007\/978-3-319-10605-2_54","volume-title":"Computer Vision \u2013 ECCV 2014","author":"J Engel","year":"2014","unstructured":"Engel, J., Sch\u00f6ps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 834\u2013849. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10605-2_54"},{"key":"14_CR6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.imavis.2017.08.002","volume":"68","author":"N Fanani","year":"2017","unstructured":"Fanani, N., St\u00fcrck, A., Ochs, M., Bradler, H., Mester, R.: Predictive monocular odometry (PMO): What is possible without RANSAC and multiframe bundle adjustment? Image Vis. Comput. 68, 3\u201313 (2017)","journal-title":"Image Vis. Comput."},{"issue":"4","key":"14_CR7","doi-asserted-by":"publisher","first-page":"1994","DOI":"10.1109\/LRA.2017.2715400","volume":"2","author":"JM Fcil","year":"2017","unstructured":"Fcil, J.M., Concha, A., Montesano, L., Civera, J.: Single-view and multi-view depth fusion. IEEE Robot. Autom. Lett. 2(4), 1994\u20132001 (2017). https:\/\/doi.org\/10.1109\/LRA.2017.2715400","journal-title":"IEEE Robot. Autom. Lett."},{"key":"14_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-46484-8_45","volume-title":"Computer Vision \u2013 ECCV 2016","author":"G Carneiro","year":"2016","unstructured":"Carneiro, G., Reid, I., Garg, R.B.G.V.K., et al.: Unsupervised CNN for single view depth estimation: geometry to the rescue. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 740\u2013756. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_45"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Garnett, N., et al.: Real-time category-based and general obstacle detection for autonomous driving. In: The IEEE International Conference on Computer Vision (ICCV), October 2017","DOI":"10.1109\/ICCVW.2017.32"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Geiger, A., Ziegler, J., Stiller, C.: Stereoscan: dense 3D reconstruction in real-time. In: 2011 IEEE Intelligent Vehicles Symposium (IV), pp. 963\u2013968. IEEE (2011)","DOI":"10.1109\/IVS.2011.5940405"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Godard, C., Mac Aodha, O., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.699"},{"volume-title":"Multiple View Geometry in Computer Vision","year":"2003","author":"R Hartley","key":"14_CR12","unstructured":"Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)"},{"key":"14_CR13","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, pp. 5574\u20135584 (2017)"},{"key":"14_CR14","unstructured":"Klappstein, J.: Optical-flow based detection of moving objects in traffic scenes. Ph.D. thesis (2008)"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Levi, D., Garnett, N., Fetaya, E., Herzlyia, I.: StixelNet: a deep convolutional network for obstacle detection and road segmentation. In: BMVC, pp. 109:1 (2015)","DOI":"10.5244\/C.29.109"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298925"},{"issue":"5","key":"14_CR18","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1109\/TRO.2015.2463671","volume":"31","author":"R Mur-Artal","year":"2015","unstructured":"Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31(5), 1147\u20131163 (2015)","journal-title":"IEEE Trans. Robot."},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Pereira, F.I., Ilha, G., Luft, J., Negreiros, M., Susin, A.: Monocular visual odometry with cyclic estimation. In: 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/SIBGRAPI.2017.7"},{"issue":"3","key":"14_CR20","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/MITS.2011.942207","volume":"3","author":"D Pfeiffer","year":"2011","unstructured":"Pfeiffer, D., Franke, U.: Modeling dynamic 3D environments by means of the Stixel World. IEEE Intell. Transp. Syst. Mag. 3(3), 24\u201336 (2011)","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Pfeiffer, D., Franke, U.: Towards a global optimal multi-layer stixel representation of dense 3D data. In: BMVC, vol. 11, pp. 51\u20131 (2011)","DOI":"10.5244\/C.25.51"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Vineet, V., Chen, Q., Koltun, V.: Dense monocular depth estimation in complex dynamic scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4058\u20134066 (2016)","DOI":"10.1109\/CVPR.2016.440"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Schneider, L., et al.: Semantic stixels: depth is not enough. In: 2016 IEEE Intelligent Vehicles Symposium (IV), pp. 110\u2013117. IEEE (2016)","DOI":"10.1109\/IVS.2016.7535373"},{"key":"14_CR24","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Tateno, K., Tombari, F., Laina, I., Navab, N.: CNN-SLAM: real-time dense monocular SLAM with learned depth prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2 (2017)","DOI":"10.1109\/CVPR.2017.695"},{"issue":"1","key":"14_CR26","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1006\/cviu.1999.0832","volume":"78","author":"PH Torr","year":"2000","unstructured":"Torr, P.H., Zisserman, A.: MLESAC: a new robust estimator with application to estimating image geometry. Comput. Vis. Image Underst. 78(1), 138\u2013156 (2000)","journal-title":"Comput. Vis. Image Underst."},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Ummenhofer, B., et al.: DeMoN: depth and motion network for learning monocular stereo. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.596"},{"key":"14_CR28","unstructured":"Vijayanarasimhan, S., Ricco, S., Schmid, C., Sukthankar, R., Fragkiadaki, K.: SfM-Net: learning of structure and motion from video. arXiv preprint arXiv:1704.07804 (2017)"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: DeepFlow: large displacement optical flow with deep matching. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1385\u20131392 (2013)","DOI":"10.1109\/ICCV.2013.175"},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: Unsupervised learning of depth and ego-motion from video. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.700"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-11009-3_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T20:07:16Z","timestamp":1674331636000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-11009-3_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030110086","9783030110093"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-11009-3_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 January 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}