{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T15:32:31Z","timestamp":1742916751861,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030816377"},{"type":"electronic","value":"9783030816384"}],"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\/978-3-030-81638-4_21","type":"book-chapter","created":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T05:03:11Z","timestamp":1626238991000},"page":"253-266","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["GCN-Calculated Graph-Feature Embedding for 3D Endoscopic System Based on Active Stereo"],"prefix":"10.1007","author":[{"given":"Michihiro","family":"Mikamo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroshi","family":"Kawasaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryusuke","family":"Sagawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryo","family":"Furukawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,15]]},"reference":[{"key":"21_CR1","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844\u20133852 (2016)"},{"issue":"1","key":"21_CR2","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s11263-006-7899-4","volume":"70","author":"PF Felzenszwalb","year":"2006","unstructured":"Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient belief propagation for early vision. Int. J. Comput. Vision 70(1), 41\u201354 (2006)","journal-title":"Int. J. Comput. Vision"},{"issue":"6","key":"21_CR3","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1049\/htl.2019.0070","volume":"6","author":"R Furukawa","year":"2019","unstructured":"Furukawa, R., et al.: Simultaneous shape and camera-projector parameter estimation for 3D endoscopic system using CNN-based grid-oneshot scan. Healthcare Technol. Lett. 6(6), 249\u2013254 (2019)","journal-title":"Healthcare Technol. Lett."},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Furukawa, R., et al.: 2-DOF auto-calibration for a 3d endoscope system based on active stereo. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7937\u20137941. IEEE (2015)","DOI":"10.1109\/EMBC.2015.7320233"},{"key":"21_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/978-3-030-01201-4_16","volume-title":"OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis","author":"R Furukawa","year":"2018","unstructured":"Furukawa, R., Mizomori, M., Hiura, S., Oka, S., Tanaka, S., Kawasaki, H.: Wide-area shape reconstruction by 3D endoscopic system based on CNN decoding, shape registration and fusion. In: Stoyanov, D., et al. (eds.) CARE\/CLIP\/OR 2.0\/ISIC -2018. LNCS, vol. 11041, pp. 139\u2013150. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01201-4_16"},{"key":"21_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1007\/978-3-319-46466-4_24","volume-title":"Computer Vision \u2013 ECCV 2016","author":"R Furukawa","year":"2016","unstructured":"Furukawa, R., Morinaga, H., Sanomura, Y., Tanaka, S., Yoshida, S., Kawasaki, H.: Shape acquisition and registration for 3d endoscope based on grid pattern projection. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 399\u2013415. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_24"},{"key":"21_CR7","doi-asserted-by":"publisher","unstructured":"Furukawa, R., et al.: Fully auto-calibrated active-stereo-based 3d endoscopic system using correspondence estimation with graph convolutional network, vol. 2020, pp. 4357\u20134360 (2020). https:\/\/doi.org\/10.1109\/EMBC44109.2020.9176417","DOI":"10.1109\/EMBC44109.2020.9176417"},{"key":"21_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/978-3-030-00937-3_17","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"J Geurten","year":"2018","unstructured":"Geurten, J., Xia, W., Jayarathne, U., Peters, T.M., Chen, E.C.S.: Endoscopic laser surface scanner for minimally invasive abdominal surgeries. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 143\u2013150. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_17"},{"key":"21_CR9","unstructured":"Kawasaki, H., Furukawa, R., Sagawa, R., Yagi, Y.: Dynamic scene shape reconstruction using a single structured light pattern. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. IEEE (2008)"},{"key":"21_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1007\/978-3-030-01201-4_15","volume-title":"OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis","author":"X Liu","year":"2018","unstructured":"Liu, X., et al.: Self-supervised learning for dense depth estimation in monocular endoscopy. In: Stoyanov, D., et al. (eds.) CARE\/CLIP\/OR 2.0\/ISIC -2018. LNCS, vol. 11041, pp. 128\u2013138. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01201-4_15"},{"key":"21_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/978-3-030-32254-0_64","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"R Ma","year":"2019","unstructured":"Ma, R., Wang, R., Pizer, S., Rosenman, J., McGill, S.K., Frahm, J.-M.: Real-time 3D reconstruction of Colonoscopic surfaces for determining missing regions. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 573\u2013582. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32254-0_64"},{"issue":"12","key":"21_CR12","doi-asserted-by":"publisher","first-page":"2572","DOI":"10.1109\/TMI.2018.2842767","volume":"37","author":"F Mahmood","year":"2018","unstructured":"Mahmood, F., Chen, R., Durr, N.J.: Unsupervised reverse domain adaptation for synthetic medical images via adversarial training. IEEE Trans. Med. Imaging 37(12), 2572\u20132581 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"21_CR13","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.media.2018.06.005","volume":"48","author":"F Mahmood","year":"2018","unstructured":"Mahmood, F., Durr, N.J.: Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy. Med. Image Anal. 48, 230\u2013243 (2018)","journal-title":"Med. Image Anal."},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Maurice, X., Albitar, C., Doignon, C., de Mathelin, M.: A structured light-based laparoscope with real-time organs\u2019 surface reconstruction for minimally invasive surgery. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5769\u20135772. IEEE (2012)","DOI":"10.1109\/EMBC.2012.6347305"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Nagakura, T., Michida, T., Hirao, M., Kawahara, K., Yamada, K.: The study of three-dimensional measurement from an endoscopic images with stereo matching method. In: 2006 World Automation Congress, pp. 1\u20134. IEEE (2006)","DOI":"10.1109\/WAC.2006.375949"},{"issue":"7","key":"21_CR16","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1007\/s11548-019-01962-w","volume":"14","author":"A Rau","year":"2019","unstructured":"Rau, A., et al.: Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy. Int. J. Comput. Assist. Radiol. Surg. 14(7), 1167\u20131176 (2019). https:\/\/doi.org\/10.1007\/s11548-019-01962-w","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"21_CR17","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). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"4","key":"21_CR18","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1016\/j.patcog.2003.10.002","volume":"37","author":"J Salvi","year":"2004","unstructured":"Salvi, J., Pages, J., Batlle, J.: Pattern codification strategies in structured light systems. Pattern Recogn. 37(4), 827\u2013849 (2004)","journal-title":"Pattern Recogn."},{"issue":"5","key":"21_CR19","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1016\/j.media.2012.04.001","volume":"16","author":"C Schmalz","year":"2012","unstructured":"Schmalz, C., Forster, F., Schick, A., Angelopoulou, E.: An endoscopic 3D scanner based on structured light. Med. Image Anal. 16(5), 1063\u20131072 (2012)","journal-title":"Med. Image Anal."},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Song, L., Tang, S., Song, Z.: A robust structured light pattern decoding method for single-shot 3D reconstruction. In: 2017 IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 668\u2013672. IEEE (2017)","DOI":"10.1109\/RCAR.2017.8311940"},{"key":"21_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/978-3-642-15705-9_34","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2010","author":"D Stoyanov","year":"2010","unstructured":"Stoyanov, D., Scarzanella, M.V., Pratt, P., Yang, G.-Z.: Real-time stereo reconstruction in robotically assisted minimally invasive surgery. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 275\u2013282. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15705-9_34"},{"issue":"7","key":"21_CR22","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1007\/s11548-017-1609-2","volume":"12","author":"M Visentini-Scarzanella","year":"2017","unstructured":"Visentini-Scarzanella, M., Sugiura, T., Kaneko, T., Koto, S.: Deep monocular 3D reconstruction for assisted navigation in bronchoscopy. Int. J. Comput. Assist. Radiol. Surg. 12(7), 1089\u20131099 (2017)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4353\u20134361 (2015)","DOI":"10.1109\/CVPR.2015.7299064"},{"issue":"1","key":"21_CR24","first-page":"2287","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), 2287\u20132318 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"21_CR25","unstructured":"Zhang, L., Curless, B., Seitz, S.M.: Rapid shape acquisition using color structured light and multi-pass dynamic programming. In: Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission, pp. 24\u201336. IEEE (2002)"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, Z.: Microsoft Kinect sensor and its effect. IEEE MultiMedia 19, 4\u201312 (2012). https:\/\/www.microsoft.com\/en-us\/research\/publication\/microsoft-kinect-sensor-and-its-effect\/","DOI":"10.1109\/MMUL.2012.24"}],"container-title":["Communications in Computer and Information Science","Frontiers of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-81638-4_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T05:24:29Z","timestamp":1626240269000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-81638-4_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030816377","9783030816384"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-81638-4_21","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"15 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IW-FCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Frontiers of Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daegu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 February 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 February 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwfcv2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/iwfcv2021\/home","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":"44","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":"17","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":"8","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":"39% - 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":"2","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)"}}]}}