{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T10:10:39Z","timestamp":1781086239699,"version":"3.54.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200465","type":"print"},{"value":"9783031200472","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\/978-3-031-20047-2_36","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T10:02:55Z","timestamp":1666432975000},"page":"627-643","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Large-Displacement 3D Object Tracking with\u00a0Hybrid Non-local Optimization"],"prefix":"10.1007","author":[{"given":"Xuhui","family":"Tian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinran","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueying","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Arvo, J.: Fast random rotation matrices. In: Graphics gems III (IBM version), pp. 117\u2013120. Elsevier (1992)","DOI":"10.1016\/B978-0-08-050755-2.50034-8"},{"key":"36_CR2","doi-asserted-by":"publisher","unstructured":"Choi, C., Christensen, H.I.: Real-time 3D model-based tracking using edge and keypoint features for robotic manipulation. In: IEEE International Conference on Robotics and Automation, pp. 4048\u20134055 (2010). https:\/\/doi.org\/10.1109\/ROBOT.2010.5509171","DOI":"10.1109\/ROBOT.2010.5509171"},{"issue":"5","key":"36_CR3","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1109\/TRO.2021.3056043","volume":"37","author":"X Deng","year":"2021","unstructured":"Deng, X., Mousavian, A., Xiang, Y., Xia, F., Bretl, T., Fox, D.: PoseRBPF: a Rao-Blackwellized particle filter for 6-D object pose tracking. IEEE Trans. Rob. 37(5), 1328\u20131342 (2021). https:\/\/doi.org\/10.1109\/TRO.2021.3056043","journal-title":"IEEE Trans. Rob."},{"issue":"7","key":"36_CR4","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1109\/TPAMI.2002.1017620","volume":"24","author":"T Drummond","year":"2002","unstructured":"Drummond, T., Cipolla, R.: Real-time visual tracking of complex structures. IEEE Trans. Patt. Anal. Mach. Intell. 24(7), 932\u2013946 (2002). https:\/\/doi.org\/10.1109\/TPAMI.2002.1017620","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Harris, C., Stennett, C.: Rapid - a video rate object tracker. In: BMVC (1990)","DOI":"10.5244\/C.4.15"},{"key":"36_CR6","doi-asserted-by":"publisher","unstructured":"Hexner, J., Hagege, R.R.: 2D\u20133D pose estimation of heterogeneous objects using a region based approach. Int. J. Comput. Vis. 118(1), 95\u2013112 (2016). https:\/\/doi.org\/10.1007\/s11263-015-0873-2","DOI":"10.1007\/s11263-015-0873-2"},{"key":"36_CR7","doi-asserted-by":"publisher","unstructured":"Huang, H., Zhong, F., Qin, X.: Pixel-wise weighted region-based 3D object tracking using contour constraints. IEEE Trans. Visual. Comput. Graph. 1 (2021). https:\/\/doi.org\/10.1109\/TVCG.2021.3085197","DOI":"10.1109\/TVCG.2021.3085197"},{"issue":"7","key":"36_CR8","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1111\/cgf.14154","volume":"39","author":"H Huang","year":"2020","unstructured":"Huang, H., Zhong, F., Sun, Y., Qin, X.: An occlusion-aware edge-based method for monocular 3D object tracking using edge confidence. Comput. Graph. Forum 39(7), 399\u2013409 (2020). https:\/\/doi.org\/10.1111\/cgf.14154","journal-title":"Comput. Graph. Forum"},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Jain, P., Kar, P.: Non-convex optimization for machine learning. arXiv preprint arXiv:1712.07897 (2017)","DOI":"10.1561\/9781680833690"},{"issue":"4","key":"36_CR10","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1109\/TPAMI.2013.170","volume":"36","author":"J Kwon","year":"2013","unstructured":"Kwon, J., Lee, H.S., Park, F.C., Lee, K.M.: A geometric particle filter for template-based visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36(4), 625\u2013643 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"36_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1007\/978-3-030-58520-4_34","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Labb\u00e9","year":"2020","unstructured":"Labb\u00e9, Y., Carpentier, J., Aubry, M., Sivic, J.: CosyPose: consistent multi-view multi-object 6D pose estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 574\u2013591. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58520-4_34"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Lepetit, V., Fua, P.: Monocular model-based 3D tracking of rigid objects. Now Publishers Inc (2005)","DOI":"10.1561\/9781933019536"},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, G., Ji, X., Xiang, Y., Fox, D.: DeepIM: deep iterative matching for 6D pose estimation. In: Proceedings of the ECCV, pp. 683\u2013698 (2018)","DOI":"10.1007\/978-3-030-01231-1_42"},{"issue":"12","key":"36_CR14","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.1109\/TVCG.2015.2513408","volume":"22","author":"E Marchand","year":"2016","unstructured":"Marchand, E., Uchiyama, H., Spindler, F.: Pose estimation for augmented reality: a hands-on survey. IEEE Trans. Vis. Comput. Graph. 22(12), 2633\u20132651 (2016). https:\/\/doi.org\/10.1109\/TVCG.2015.2513408","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"36_CR15","doi-asserted-by":"publisher","unstructured":"Peng, S., Liu, Y., Huang, Q., Zhou, X., Bao, H.: PVNet: pixel-wise voting network for 6DoF pose estimation. In: IEEE\/CVF Conference on CVPR, pp. 4556\u20134565. IEEE, Long Beach, CA, USA, June 2019. https:\/\/doi.org\/10.1109\/CVPR.2019.00469","DOI":"10.1109\/CVPR.2019.00469"},{"key":"36_CR16","doi-asserted-by":"publisher","unstructured":"Prisacariu, V., Reid, I.: PWP3D: real-time segmentation and tracking of 3D objects. In: Proceedings of the 20th British Machine Vision Conference (September 2009). https:\/\/doi.org\/10.1007\/s11263-011-0514-3","DOI":"10.1007\/s11263-011-0514-3"},{"issue":"1","key":"36_CR17","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1109\/TVCG.2013.94","volume":"20","author":"BK Seo","year":"2014","unstructured":"Seo, B.K., Park, H., Park, J.I., Hinterstoisser, S., Ilic, S.: Optimal local searching for fast and robust textureless 3D object tracking in highly cluttered backgrounds. IEEE Trans. Vis. Comput. Graph. 20(1), 99\u2013110 (2014). https:\/\/doi.org\/10.1109\/TVCG.2013.94","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Stoiber, M., Pfanne, M., Strobl, K.H., Triebel, R., Albu-Schaeffer, A.: A sparse gaussian approach to region-based 6DoF object tracking. In: Proceedings of the Asian Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-69532-3_40"},{"issue":"4","key":"36_CR19","doi-asserted-by":"publisher","first-page":"1008","DOI":"10.1007\/s11263-022-01579-8","volume":"130","author":"M Stoiber","year":"2022","unstructured":"Stoiber, M., Pfanne, M., Strobl, K.H., Triebel, R., Albu-Sch\u00e4ffer, A.: SRT3D: a sparse region-based 3D object tracking approach for the real world. Int. J. Comput. Vis. 130(4), 1008\u20131030 (2022). https:\/\/doi.org\/10.1007\/s11263-022-01579-8","journal-title":"Int. J. Comput. Vis."},{"issue":"11","key":"36_CR20","doi-asserted-by":"publisher","first-page":"4409","DOI":"10.1109\/TCSVT.2021.3053696","volume":"31","author":"X Sun","year":"2021","unstructured":"Sun, X., Zhou, J., Zhang, W., Wang, Z., Yu, Q.: Robust monocular pose tracking of less-distinct objects based on contour-part model. IEEE Trans. Circuits Syst. Video Technol. 31(11), 4409\u20134421 (2021). https:\/\/doi.org\/10.1109\/TCSVT.2021.3053696","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"36_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1007\/978-3-319-46493-0_26","volume-title":"Computer Vision \u2013 ECCV 2016","author":"H Tjaden","year":"2016","unstructured":"Tjaden, H., Schwanecke, U., Sch\u00f6mer, E.: Real-time monocular segmentation and pose tracking of multiple objects. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 423\u2013438. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_26"},{"issue":"8","key":"36_CR22","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1109\/TPAMI.2018.2884990","volume":"41","author":"H Tjaden","year":"2019","unstructured":"Tjaden, H., Schwanecke, U., Schomer, E., Cremers, D.: A region-based gauss-newton approach to real-time monocular multiple object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1797\u20131812 (2019). https:\/\/doi.org\/10.1109\/TPAMI.2018.2884990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"36_CR23","doi-asserted-by":"publisher","unstructured":"Tjaden, H., Schwanecke, U., Sch\u00f6mer, E.: Real-time monocular pose estimation of 3D objects using temporally consistent local color histograms. In: IEEE International Conference on Computer Vision (ICCV), pp. 124\u2013132 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.23","DOI":"10.1109\/ICCV.2017.23"},{"key":"36_CR24","doi-asserted-by":"publisher","unstructured":"Vacchetti, L., Lepetit, V., Fua, P.: Combining edge and texture information for real-time accurate 3D camera tracking. In: IEEE and ACM International Symposium on Mixed and Augmented Reality, pp. 48\u201356 (2004). https:\/\/doi.org\/10.1109\/ISMAR.2004.24","DOI":"10.1109\/ISMAR.2004.24"},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Wen, B., Bekris, K.: BundleTrack: 6D pose tracking for novel objects without instance or category-level 3D models. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8067\u20138074. IEEE (2021)","DOI":"10.1109\/IROS51168.2021.9635991"},{"key":"36_CR26","doi-asserted-by":"crossref","unstructured":"Wen, B., Mitash, C., Ren, B., Bekris, K.E.: se(3)-TrackNet: data-driven 6d pose tracking by calibrating image residuals in synthetic domains. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10367\u201310373. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9341314"},{"key":"36_CR27","doi-asserted-by":"publisher","unstructured":"Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: PoseCNN: a convolutional neural network for 6D object pose estimation in cluttered scenes. In: Robotics: Science and Systems XIV. Robotics: Science and Systems Foundation, June 2018. https:\/\/doi.org\/10.15607\/RSS.2018.XIV.019","DOI":"10.15607\/RSS.2018.XIV.019"},{"issue":"4","key":"36_CR28","first-page":"1","volume":"40","author":"J Zhang","year":"2021","unstructured":"Zhang, J., Zhu, C., Zheng, L., Xu, K.: ROSEFusion: random optimization for online dense reconstruction under fast camera motion. ACM Trans. Graph. (TOG) 40(4), 1\u201317 (2021)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"36_CR29","doi-asserted-by":"publisher","first-page":"5065","DOI":"10.1109\/TIP.2020.2973512","volume":"29","author":"L Zhong","year":"2020","unstructured":"Zhong, L., Zhao, X., Zhang, Y., Zhang, S., Zhang, L.: Occlusion-aware region-based 3D pose tracking of objects with temporally consistent polar-based local partitioning. IEEE Trans. Image Process. 29, 5065\u20135078 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.2973512","journal-title":"IEEE Trans. Image Process."}],"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\/978-3-031-20047-2_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T10:03:33Z","timestamp":1728209013000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20047-2_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200465","9783031200472"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20047-2_36","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)"}}]}}