{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:40:21Z","timestamp":1765546821709,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030890285"},{"type":"electronic","value":"9783030890292"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-89029-2_9","type":"book-chapter","created":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T05:09:24Z","timestamp":1633928964000},"page":"113-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Monocular Dense SLAM with Consistent Deep Depth Prediction"],"prefix":"10.1007","author":[{"given":"Feihu","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawei","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoxin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,11]]},"reference":[{"issue":"2","key":"9_CR1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1145\/335191.335388","volume":"29","author":"MM Breunig","year":"2000","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. SIGMOD Rec. 29(2), 93\u2013104 (2000)","journal-title":"SIGMOD Rec."},{"issue":"6","key":"9_CR2","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","volume":"32","author":"C Cadena","year":"2016","unstructured":"Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., Leonard, J.J.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Rob. 32(6), 1309\u20131332 (2016)","journal-title":"IEEE Trans. Rob."},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Concha, A., Civera, J.: Dense piecewise planar tracking and mapping from a monocular sequence. In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS) (2015)","DOI":"10.1109\/IROS.2015.7354184"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Deng, X., Zhang, Z., Sintov, A., Huang, J., Bretl, T.: Feature-constrained active visual slam for mobile robot navigation. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 7233\u20137238 (2018)","DOI":"10.1109\/ICRA.2018.8460721"},{"issue":"3","key":"9_CR5","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1109\/TPAMI.2017.2658577","volume":"40","author":"J Engel","year":"2018","unstructured":"Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611\u2013625 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Engel, J., Sch\u00f6ps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: European Conference on Computer Vision (ECCV) (2014)","DOI":"10.1007\/978-3-319-10605-2_54"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Godard, C., Aodha, O.M., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6602\u20136611 (2017)","DOI":"10.1109\/CVPR.2017.699"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Hermans, A., Floros, G., Leibe, B.: Dense 3d semantic mapping of indoor scenes from rgb-d images. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2631\u20132638 (2014)","DOI":"10.1109\/ICRA.2014.6907236"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Ji, X., Ye, X., Xu, H., Li, H.: Dense reconstruction from monocular slam with fusion of sparse map-points and cnn-inferred depth. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136 (2018)","DOI":"10.1109\/ICME.2018.8486548"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Klein, G., Murray, D.: Parallel tracking and mapping for small ar workspaces. In: 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp. 225\u2013234 (2007)","DOI":"10.1109\/ISMAR.2007.4538852"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhang, G., Bao, H.: Robust keyframe-based monocular slam for augmented reality. In: 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 1\u201310 (2016)","DOI":"10.1109\/ISMAR.2016.24"},{"issue":"5","key":"9_CR13","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., Tard\u00f3s, J.D.: Orb-slam: a versatile and accurate monocular slam system. IEEE Trans. Rob. 31(5), 1147\u20131163 (2015)","journal-title":"IEEE Trans. Rob."},{"issue":"5","key":"9_CR14","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1109\/TRO.2017.2705103","volume":"33","author":"R Mur-Artal","year":"2017","unstructured":"Mur-Artal, R., Tard\u00f3s, J.D.: Orb-slam2: an open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Trans. Rob. 33(5), 1255\u20131262 (2017)","journal-title":"IEEE Trans. Rob."},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., et al.: Kinectfusion: real-time densesurface mapping and tracking. In: 2011 10th IEEE International Symposium on Mixed and Augmented Reality, pp. 127\u2013136 (2011)","DOI":"10.1109\/ISMAR.2011.6162880"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: Dtam: dense tracking and mapping in real-time. In: 2011 International Conference on Computer Vision, pp. 2320\u20132327 (2011)","DOI":"10.1109\/ICCV.2011.6126513"},{"key":"9_CR17","first-page":"1","volume":"70","author":"Y Pan","year":"2021","unstructured":"Pan, Y., Xu, X., Ding, X., Huang, S., Wang, Y., Xiong, R.: Gem: online globally consistent dense elevation mapping for unstructured terrain. IEEE Trans. Instrum. Meas. 70, 1\u201313 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"9_CR18","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: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.695"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Teixeira, L., Chli, M.: Real-time local 3d reconstruction for aerial inspection using superpixel expansion. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 4560\u20134567 (2017)","DOI":"10.1109\/ICRA.2017.7989530"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Wang, K., Ding, W., Shen, S.: Quadtree-accelerated real-time monocular dense mapping. In: 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1\u20139 (2018)","DOI":"10.1109\/IROS.2018.8594101"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Wang, K., Gao, F., Shen, S.: Real-time scalable dense surfel mapping. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 6919\u20136925 (2019)","DOI":"10.1109\/ICRA.2019.8794101"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Xue, T., Luo, H., Cheng, D., Yuan, Z., Yang, X.: Real-time monocular dense mapping for augmented reality. In: Proceedings of the 25th ACM International Conference on Multimedia, MM 2017, pp. 510\u2013518. Association for Computing Machinery, New York (2017)","DOI":"10.1145\/3123266.3123348"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Yin, X., Wang, X., Du, X., Chen, Q.: Scale recovery for monocular visual odometry using depth estimated with deep convolutional neural fields. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5871\u20135879 (2017)","DOI":"10.1109\/ICCV.2017.625"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Younes, G., Asmar, D.C., Shammas, E.A.: A survey on non-filter-based monocular visual SLAM systems. CoRR abs\/1607.00470 (2016)","DOI":"10.15353\/vsnl.v2i1.109"}],"container-title":["Lecture Notes in Computer Science","Advances in Computer Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89029-2_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T18:03:32Z","timestamp":1673460212000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89029-2_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030890285","9783030890292"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89029-2_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"11 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Computer Graphics International Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"38","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cgi2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cgs-network.org\/cgi21\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"131","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"9","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}