{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T05:20:45Z","timestamp":1743052845408,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"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_41","type":"book-chapter","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T13:20:30Z","timestamp":1572009630000},"page":"581-594","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Learning 3D Semantic Reconstruction on Octrees"],"prefix":"10.1007","author":[{"given":"Xiaojuan","family":"Wang","sequence":"first","affiliation":[]},{"given":"Martin R.","family":"Oswald","sequence":"additional","affiliation":[]},{"given":"Ian","family":"Cherabier","sequence":"additional","affiliation":[]},{"given":"Marc","family":"Pollefeys","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,25]]},"reference":[{"issue":"2","key":"41_CR1","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s10851-007-0002-0","volume":"28","author":"X Bresson","year":"2007","unstructured":"Bresson, X., Esedog\u0304lu, S., Vandergheynst, P., Thiran, J.P., Osher, S.: Fastglobal minimization of the active contour\/snake model. J. Math. Imaging Vis. 28(2), 151\u2013167 (2007)","journal-title":"J. Math. Imaging Vis."},{"issue":"5","key":"41_CR2","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1137\/040615286","volume":"66","author":"T Chan","year":"2006","unstructured":"Chan, T., Esedog\u0304lu, S., Nikolova, M.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM J. Appl. Math. 66(5), 1362\u20131648 (2006)","journal-title":"SIAM J. Appl. Math."},{"key":"41_CR3","unstructured":"Chen, L.C., et al.: Searching for efficient multi-scale architectures for dense image prediction. In: Proceedings of Neural Information Processing Systems (NIPS) (2018)"},{"key":"41_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"41_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/978-3-030-01258-8_20","volume-title":"Computer Vision \u2013 ECCV 2018","author":"I Cherabier","year":"2018","unstructured":"Cherabier, I., Sch\u00f6nberger, J.L., Oswald, M.R., Pollefeys, M., Geiger, A.: Learning priors for semantic 3D reconstruction. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 325\u2013341. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-030-01258-8_20"},{"key":"41_CR6","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nie\u00dfner, M.: ScanNet: richly-annotated 3D reconstructions of indoor scenes. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.261"},{"key":"41_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1007\/978-3-030-01249-6_28","volume-title":"Computer Vision \u2013 ECCV 2018","author":"A Dai","year":"2018","unstructured":"Dai, A., Nie\u00dfner, M.: 3DMV: joint 3D-multi-view prediction for 3D semantic scene segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 458\u2013474. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-030-01249-6_28"},{"key":"41_CR8","doi-asserted-by":"crossref","unstructured":"Gargantini, I.: Linear octree for fast processing of three-dimensional objects. Comput. Graph. Image Process. 20 (1982)","DOI":"10.1016\/0146-664X(82)90058-2"},{"key":"41_CR9","doi-asserted-by":"publisher","unstructured":"H\u00e4ne, C., Zach, C., Cohen, A., Angst, R., Pollefeys, M.: Joint 3D scene reconstruction and class segmentation. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 97\u2013104 (2013). \n                      https:\/\/doi.org\/10.1109\/CVPR.2013.20","DOI":"10.1109\/CVPR.2013.20"},{"issue":"9","key":"41_CR10","doi-asserted-by":"publisher","first-page":"1730","DOI":"10.1109\/TPAMI.2016.2613051","volume":"39","author":"C H\u00e4ne","year":"2017","unstructured":"H\u00e4ne, C., Zach, C., Cohen, A., Pollefeys, M.: Dense semantic 3D reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 39(9), 1730\u20131743 (2017). \n                      https:\/\/doi.org\/10.1109\/TPAMI.2016.2613051","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"41_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: Proceedings of International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"41_CR12","doi-asserted-by":"crossref","unstructured":"Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint \n                      arXiv:1408.5093\n                      \n                     (2014)","DOI":"10.1145\/2647868.2654889"},{"key":"41_CR13","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"41_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1007\/978-3-642-33715-4_54","volume-title":"Computer Vision \u2013 ECCV 2012","author":"N Silberman","year":"2012","unstructured":"Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746\u2013760. Springer, Heidelberg (2012). \n                      https:\/\/doi.org\/10.1007\/978-3-642-33715-4_54"},{"key":"41_CR15","doi-asserted-by":"crossref","unstructured":"Pock, T., Chambolle, A.: Diagonal preconditioning for first order primal-dual algorithms in convex optimization. In: International Conference on Computer Vision (ICCV) (2011)","DOI":"10.1109\/ICCV.2011.6126441"},{"key":"41_CR16","doi-asserted-by":"crossref","unstructured":"Riegler, G., Ulusoy, A.O., Bischof, H., Geiger, A.: OctNetFusion: learning depth fusion from data. In: International Conference on 3D Vision (3DV) (2017)","DOI":"10.1109\/3DV.2017.00017"},{"key":"41_CR17","doi-asserted-by":"crossref","unstructured":"Riegler, G., Ulusoy, A.O., Geiger, A.: OctNet: learning deep 3D representations at high resolutions. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.701"},{"key":"41_CR18","doi-asserted-by":"crossref","unstructured":"Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M., Funkhouser, T.A.: Semantic scene completion from a single depth image. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.28"},{"key":"41_CR19","unstructured":"Tatarchenko, M., Dosovitskiy, A., Brox, T.: Octree generating networks: Efficient convolutional architectures for high-resolution 3D outputs. In: Proceedings of International Conference on Computer Vision (ICCV) (2017). \n                      http:\/\/lmb.informatik.uni-freiburg.de\/Publications\/2017\/TDB17b"},{"issue":"4","key":"41_CR20","first-page":"72","volume":"36","author":"PS Wang","year":"2017","unstructured":"Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: O-CNN: octree-based Convolutional neural networks for 3D shape analysis. ACM Trans. Graph. (SIGGRAPH) 36(4), 72 (2017)","journal-title":"ACM Trans. Graph. (SIGGRAPH)"},{"key":"41_CR21","doi-asserted-by":"crossref","unstructured":"Wang, P.S., Sun, C.Y., Liu, Y., Tong, X.: Adaptive O-CNN: a patch-based deep representation of 3D shapes. ACM Transactions on Graphics (SIGGRAPH Asia), vol. 37, no. 6 (2018)","DOI":"10.1145\/3272127.3275050"}],"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_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T16:31:48Z","timestamp":1572021108000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-33676-9_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030336752","9783030336769"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33676-9_41","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)"}}]}}