{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:08:11Z","timestamp":1742911691149,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030336752"},{"type":"electronic","value":"9783030336769"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-33676-9_1","type":"book-chapter","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T13:20:30Z","timestamp":1572009630000},"page":"3-17","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Learned Collaborative Stereo Refinement"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2371-014X","authenticated-orcid":false,"given":"Patrick","family":"Kn\u00f6belreiter","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6120-1058","authenticated-orcid":false,"given":"Thomas","family":"Pock","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,25]]},"reference":[{"key":"1_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1007\/978-3-319-46487-9_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"JT Barron","year":"2016","unstructured":"Barron, J.T., Poole, B.: The fast bilateral solver. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 617\u2013632. Springer, Cham (2016). \n                      https:\/\/doi.org\/10.1007\/978-3-319-46487-9_38"},{"key":"1_CR2","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1137\/080716542","volume":"2","author":"A Beck","year":"2009","unstructured":"Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2, 183\u2013202 (2009)","journal-title":"SIAM J. Imaging Sci."},{"key":"1_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-540-24673-2_3","volume-title":"Computer Vision - ECCV 2004","author":"T Brox","year":"2004","unstructured":"Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25\u201336. Springer, Heidelberg (2004). \n                      https:\/\/doi.org\/10.1007\/978-3-540-24673-2_3"},{"key":"1_CR4","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/s10851-010-0251-1","volume":"40","author":"A Chambolle","year":"2011","unstructured":"Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. 40, 120\u2013145 (2011)","journal-title":"J. Math. Imaging Vis."},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Chang, J.R., Chen, Y.S.: Pyramid stereo matching network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5410\u20135418 (2018)","DOI":"10.1109\/CVPR.2018.00567"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yu, W., Pock, T.: On learning optimized reaction diffusion processes for effective image restoration. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5261\u20135269 (2015)","DOI":"10.1109\/CVPR.2015.7299163"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Detect, replace, refine: deep structured prediction for pixel wise labeling. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5248\u20135257 (2017)","DOI":"10.1109\/CVPR.2017.760"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"2121","DOI":"10.1109\/TPAMI.2011.283","volume":"34","author":"X Hu","year":"2012","unstructured":"Hu, X., Mordohai, P.: A quantitative evaluation of confidence measures for stereo vision. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2121\u20132133 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint \n                      arXiv:1412.6980\n                      \n                     (2014)"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Kn\u00f6belreiter, P., Reinbacher, C., Shekhovtsov, A., Pock, T.: End-to-end training of hybrid CNN-CRF models for stereo. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2339\u20132348 (2017)","DOI":"10.1109\/CVPR.2017.159"},{"key":"1_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/978-3-319-66709-6_23","volume-title":"Pattern Recognition","author":"E Kobler","year":"2017","unstructured":"Kobler, E., Klatzer, T., Hammernik, K., Pock, T.: Variational networks: connecting variational methods and deep learning. In: Roth, V., Vetter, T. (eds.) GCPR 2017. LNCS, vol. 10496, pp. 281\u2013293. Springer, Cham (2017). \n                      https:\/\/doi.org\/10.1007\/978-3-319-66709-6_23"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Kuschk, G., Cremers, D.: Fast and accurate large-scale stereo reconstruction using variational methods. In: IEEE International Conference on Computer Vision Workshop, pp. 700\u2013707 (2013)","DOI":"10.1109\/ICCVW.2013.96"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Liang, Z., et al.: Learning for disparity estimation through feature constancy. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2811\u20132820 (2018)","DOI":"10.1109\/CVPR.2018.00297"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Maurer, D., Stoll, M., Bruhn, A.: Order-adaptive and illumination-aware variational optical flow refinement. In: British Machine Vision Conference (2017)","DOI":"10.5244\/C.31.150"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3061\u20133070 (2015)","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Pang, J., Sun, W., Ren, J.S., Yang, C., Yan, Q.: Cascade residual learning: a two-stage convolutional neural network for stereo matching. In: IEEE International Conference on Computer Vision Workshop, pp. 887\u2013895 (2017)","DOI":"10.1109\/ICCVW.2017.108"},{"key":"1_CR19","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1561\/2400000003","volume":"1","author":"N Parikh","year":"2014","unstructured":"Parikh, N., Boyd, S., et al.: Proximal algorithms. Found. Trends Optim. 1, 127\u2013239 (2014)","journal-title":"Found. Trends Optim."},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Gehrig, S., Pock, T., Bischof, H.: Pushing the limits of stereo using variational stereo estimation. In: IEEE Intelligent Vehicles Symposium, pp. 401\u2013407 (2012)","DOI":"10.1109\/IVS.2012.6232171"},{"key":"1_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/978-3-319-10590-1_29","volume-title":"Computer Vision \u2013 ECCV 2014","author":"R Ranftl","year":"2014","unstructured":"Ranftl, R., Bredies, K., Pock, T.: Non-local total generalized variation for optical flow estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 439\u2013454. Springer, Cham (2014). \n                      https:\/\/doi.org\/10.1007\/978-3-319-10590-1_29"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: Epicflow: edge-preserving interpolation of correspondences for optical flow. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1164\u20131172 (2015)","DOI":"10.1109\/CVPR.2015.7298720"},{"key":"1_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1007\/978-3-319-46487-9_17","volume-title":"Computer Vision \u2013 ECCV 2016","author":"G Riegler","year":"2016","unstructured":"Riegler, G., R\u00fcther, M., Bischof, H.: ATGV-Net: accurate depth super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 268\u2013284. Springer, Cham (2016). \n                      https:\/\/doi.org\/10.1007\/978-3-319-46487-9_17"},{"key":"1_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). \n                      https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"1_CR25","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s11263-008-0197-6","volume":"82","author":"S Roth","year":"2009","unstructured":"Roth, S., Black, M.J.: Fields of experts. Int. J. Comput. Vis. 82, 205 (2009)","journal-title":"Int. J. Comput. Vis."},{"key":"1_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/978-3-319-11752-2_3","volume-title":"Pattern Recognition","author":"D Scharstein","year":"2014","unstructured":"Scharstein, D., et al.: High-resolution stereo datasets with subpixel-accurate ground truth. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 31\u201342. Springer, Cham (2014). \n                      https:\/\/doi.org\/10.1007\/978-3-319-11752-2_3"},{"key":"1_CR27","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1023\/A:1014573219977","volume":"47","author":"D Scharstein","year":"2002","unstructured":"Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47, 7\u201342 (2002)","journal-title":"Int. J. Comput. Vis."},{"key":"1_CR28","unstructured":"Shekhovtsov, A., Reinbacher, C., Graber, G., Pock, T.: Solving dense image matching in real-time using discrete-continuous optimization. In: Computer Vision Winter Workshop (2016)"},{"key":"1_CR29","unstructured":"Tulyakov, S., Ivanov, A., Fleuret, F.: Practical deep stereo (pds): toward applications-friendly deep stereo matching. In: Proceedings of Advances in Neural Information Processing Systems, pp. 5871\u20135881 (2018)"},{"key":"1_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1007\/978-3-030-20876-9_22","volume-title":"Computer Vision \u2013 ACCV 2018","author":"C Vogel","year":"2019","unstructured":"Vogel, C., Kn\u00f6belreiter, P., Pock, T.: Learning energy based inpainting for optical flow. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11366, pp. 340\u2013356. Springer, Cham (2019). \n                      https:\/\/doi.org\/10.1007\/978-3-030-20876-9_22"},{"key":"1_CR31","doi-asserted-by":"crossref","unstructured":"Vogel, C., Pock, T.: A primal dual network for low-level vision problems. In: German Conference on Pattern Recognition (GCPR) (2017)","DOI":"10.1007\/978-3-319-66709-6_16"},{"key":"1_CR32","unstructured":"Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-L1 optical flow. In: German Conference on Pattern Recognition (GCPR) (2007)"},{"key":"1_CR33","first-page":"1","volume":"17","author":"J \u017dbontar","year":"2016","unstructured":"\u017dbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res. 17, 1\u201332 (2016)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33676-9_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T16:21:02Z","timestamp":1572020462000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-33676-9_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030336752","9783030336769"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33676-9_1","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":"25 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAGM GCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"German Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dortmund","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"41","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dagm2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/gcpr2019.tu-dortmund.de\/","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":"91","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":"43","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":"47% - 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":"5","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)"}}]}}