{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:14:56Z","timestamp":1774541696579,"version":"3.50.1"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198380","type":"print"},{"value":"9783031198397","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-19839-7_34","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T11:40:06Z","timestamp":1666438806000},"page":"586-602","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["BRNet: Exploring Comprehensive Features for\u00a0Monocular Depth Estimation"],"prefix":"10.1007","author":[{"given":"Wencheng","family":"Han","sequence":"first","affiliation":[]},{"given":"Junbo","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Xiaogang","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Xiangdong","family":"Dai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1883-2086","authenticated-orcid":false,"given":"Jianbing","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"34_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/978-3-030-11009-3_20","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"F Aleotti","year":"2019","unstructured":"Aleotti, F., Tosi, F., Poggi, M., Mattoccia, S.: Generative adversarial networks for unsupervised monocular depth prediction. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11129, pp. 337\u2013354. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11009-3_20"},{"key":"34_CR2","unstructured":"Bhat, S.F., Alhashim, I., Wonka, P.: AdaBins: depth estimation using adaptive bins. In: CVPR (2021)"},{"key":"34_CR3","doi-asserted-by":"crossref","unstructured":"Casser, V., Pirk, S., Mahjourian, R., Angelova, A.: Depth prediction without the sensors: leveraging structure for unsupervised learning from monocular videos. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.33018001"},{"key":"34_CR4","doi-asserted-by":"crossref","unstructured":"Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.304"},{"key":"34_CR5","unstructured":"Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. arXiv preprint arXiv:1406.2283 (2014)"},{"key":"34_CR6","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":"R Garg","year":"2016","unstructured":"Garg, R., B.G., V.K., Carneiro, G., Reid, I.: 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":"34_CR7","doi-asserted-by":"crossref","unstructured":"Godard, C., Aodha, O.M., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.699"},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Godard, C., Aodha, O.M., Firman, M., Brostow, G.J.: Digging into self-supervised monocular depth estimation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00393"},{"key":"34_CR9","doi-asserted-by":"crossref","unstructured":"Guizilini, V., Ambrus, R., Pillai, S., Raventos, A., Gaidon, A.: 3D packing for self-supervised monocular depth estimation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00256"},{"key":"34_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1007\/978-3-030-01270-0_42","volume-title":"Computer Vision \u2013 ECCV 2018","author":"J Janai","year":"2018","unstructured":"Janai, J., G\u00fcney, F., Ranjan, A., Black, M., Geiger, A.: Unsupervised learning of multi-frame optical flow with occlusions. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 713\u2013731. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01270-0_42"},{"key":"34_CR11","doi-asserted-by":"crossref","unstructured":"Johnston, A., Carneiro, G.: Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00481"},{"key":"34_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1007\/978-3-030-01249-6_43","volume-title":"Computer Vision \u2013 ECCV 2018","author":"M Klodt","year":"2018","unstructured":"Klodt, M., Vedaldi, A.: Supervising the new with the old: learning SFM from SFM. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 713\u2013728. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_43"},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Kuznietsov, Y., Stuckler, J., Leibe, B.: Semi-supervised deep learning for monocular depth map prediction. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.238"},{"key":"34_CR14","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"10","key":"34_CR15","doi-asserted-by":"publisher","first-page":"2624","DOI":"10.1109\/TPAMI.2019.2930258","volume":"42","author":"C Luo","year":"2019","unstructured":"Luo, C., et al.: Every pixel counts++: joint learning of geometry and motion with 3D holistic understanding. PAMI 42(10), 2624\u20132641 (2019)","journal-title":"PAMI"},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Luo, Y., et al.: Single view stereo matching. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00024"},{"key":"34_CR17","unstructured":"Lyu, X., et al.: HR-depth: high resolution self-supervised monocular depth estimation. CoRR abs\/2012.07356 (2020)"},{"key":"34_CR18","doi-asserted-by":"crossref","unstructured":"Mahjourian, R., Wicke, M., Angelova, A.: Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00594"},{"key":"34_CR19","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.865","volume":"8","author":"I Makarov","year":"2022","unstructured":"Makarov, I., Bakhanova, M., Nikolenko, S., Gerasimova, O.: Self-supervised recurrent depth estimation with attention mechanisms. PeerJ Comput. Sci. 8, e865 (2022)","journal-title":"PeerJ Comput. Sci."},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"Mahdi, S., Miangoleh, H., Dille, S., Mai, L., Paris, S., Aksoy, Y.: Boosting monocular depth estimation models to high-resolution via content-adaptive multi-resolution merging. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00956"},{"key":"34_CR21","doi-asserted-by":"crossref","unstructured":"Pillai, S., Ambru\u015f, R., Gaidon, A.: SuperDepth: self-supervised, super-resolved monocular depth estimation. In: ICRA (2019)","DOI":"10.1109\/ICRA.2019.8793621"},{"key":"34_CR22","doi-asserted-by":"crossref","unstructured":"Poggi, M., Aleotti, F., Tosi, F., Mattoccia, S.: Towards real-time unsupervised monocular depth estimation on CPU. In: IROS (2018)","DOI":"10.1109\/IROS.2018.8593814"},{"key":"34_CR23","doi-asserted-by":"crossref","unstructured":"Poggi, M., Tosi, F., Mattoccia, S.: Learning monocular depth estimation with unsupervised trinocular assumptions. In: 3DV (2018)","DOI":"10.1109\/3DV.2018.00045"},{"key":"34_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1007\/978-3-030-20893-6_19","volume-title":"Computer Vision \u2013 ACCV 2018","author":"P Zama Ramirez","year":"2019","unstructured":"Zama Ramirez, P., Poggi, M., Tosi, F., Mattoccia, S., Di Stefano, L.: Geometry meets semantics for semi-supervised monocular depth estimation. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 298\u2013313. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_19"},{"key":"34_CR25","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: CVPR (2021)","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"34_CR26","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"34_CR27","doi-asserted-by":"crossref","unstructured":"Saxena, A., Sun, M., Ng, A.Y.: Make3D: depth perception from a single still image. In: AAAI (2008)","DOI":"10.1007\/s11263-007-0071-y"},{"key":"34_CR28","doi-asserted-by":"crossref","unstructured":"Saxena, A., Sun, M., Ng, A.Y.: Make3D: learning 3D scene structure from a single still image. PAMI 31(5), 824\u2013840 (2008)","DOI":"10.1109\/TPAMI.2008.132"},{"key":"34_CR29","unstructured":"Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: CVPR (2003)"},{"key":"34_CR30","doi-asserted-by":"crossref","unstructured":"Shelhamer, E., Barron, J.T., Darrell, T.: Scene intrinsics and depth from a single image. In: ICCV Workshops (2015)","DOI":"10.1109\/ICCVW.2015.39"},{"key":"34_CR31","doi-asserted-by":"crossref","unstructured":"Song, M., Lim, S., Kim, W.: Monocular depth estimation using Laplacian pyramid-based depth residuals. IEEE Trans. Circuits Syst. Video Technol. (2021)","DOI":"10.1109\/TCSVT.2021.3049869"},{"key":"34_CR32","doi-asserted-by":"crossref","unstructured":"Tosi, F., Aleotti, F., Poggi, M., Mattoccia, S.: Learning monocular depth estimation infusing traditional stereo knowledge. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01003"},{"key":"34_CR33","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"34_CR34","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":"34_CR35","doi-asserted-by":"crossref","unstructured":"Watson, J., Firman, M., Brostow, G.J., Turmukhambetov, D.: Self-supervised monocular depth hints. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00225"},{"key":"34_CR36","doi-asserted-by":"crossref","unstructured":"Yang, Z., Wang, P., Wang, Y., Xu, W., Nevatia, R.: Lego: learning edge with geometry all at once by watching videos. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00031"},{"key":"34_CR37","doi-asserted-by":"crossref","unstructured":"Yang, Z., Wang, P., Xu, W., Zhao, L., Nevatia, R.: Unsupervised learning of geometry with edge-aware depth-normal consistency. arXiv preprint arXiv:1711.03665 (2017)","DOI":"10.1609\/aaai.v32i1.12257"},{"issue":"1","key":"34_CR38","first-page":"2287","volume":"17","author":"J Zbontar","year":"2016","unstructured":"Zbontar, J., LeCun, Y., et al.: Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res. 17(1), 2287\u20132318 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"34_CR39","doi-asserted-by":"crossref","unstructured":"Zhan, H., Garg, R., Weerasekera, C.S., Li, K., Agarwal, H., Reid, I.: Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction. In CVPR (2018)","DOI":"10.1109\/CVPR.2018.00043"},{"key":"34_CR40","doi-asserted-by":"crossref","unstructured":"Zhao, W., Liu, S., Shu, Y., Liu, Y.-J.: Towards better generalization: joint depth-pose learning without posenet. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00917"},{"key":"34_CR41","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"},{"key":"34_CR42","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Fan, X., Shi, P., Xin, Y.: R-MSFM: recurrent multi-scale feature modulation for monocular depth estimating. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01254"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19839-7_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:45:10Z","timestamp":1709811910000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19839-7_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198380","9783031198397"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19839-7_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 October 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"0","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}