{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:32:16Z","timestamp":1743064336057,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031064296"},{"type":"electronic","value":"9783031064302"}],"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\/978-3-031-06430-2_3","type":"book-chapter","created":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T08:03:16Z","timestamp":1652688196000},"page":"27-38","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["3D Key-Points Estimation from\u00a0Single-View RGB Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2259-4121","authenticated-orcid":false,"given":"Mohammad","family":"Zohaib","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1759-2447","authenticated-orcid":false,"given":"Matteo","family":"Taiana","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0342-5448","authenticated-orcid":false,"given":"Milind Gajanan","family":"Padalkar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2262-4872","authenticated-orcid":false,"given":"Alessio","family":"Del Bue","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,17]]},"reference":[{"key":"3_CR1","unstructured":"Suwajanakorn, S., Snavely, N., Tompson, J., Norouzi, M.: Discovery of latent 3D keypoints via end-to-end geometric reasoning. In: NeurIPS (2018)"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Spezialetti, R., Salti, S. and Di Stefano, L.: Performance evaluation of 3D descriptors paired with learned keypoint detectors. AI 2(2), pp. 229\u2013243 (2021)","DOI":"10.3390\/ai2020014"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"You, Y., et al.: KeypointNet: a large-scale 3D keypoint dataset aggregated from numerous human annotations. In: CVPR, pp. 13647\u201313656 (2020)","DOI":"10.1109\/CVPR42600.2020.01366"},{"key":"3_CR4","unstructured":"Bisio, I., Haleem, H., Garibotto, C., Lavagetto, F., Sciarrone, A.: Performance evaluation and analysis of drone-based vehicle detection techniques from deep learning perspective. IEEE Internet Things J. 14(8) (2021)"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Shu, Z., et al.: Detecting 3D points of interest using projective neural networks. IEEE Trans. Multimed. (2021)","DOI":"10.1109\/TMM.2021.3070977"},{"key":"3_CR6","unstructured":"Lin, Y., Chen, L., Huang, H., Ma, C., Han, X., Cui, S.: Beyond farthest point sampling in point-wise analysis. arXiv preprint arXiv:2107.04291 (2021)"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Yu, T., Dai, Q., Liu, Y.: Deep implicit templates for 3D shape representation. In: CVPR, pp. 1429\u20131439 (2021)","DOI":"10.1109\/CVPR46437.2021.00148"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Zhao, W., Zhang, S., Guan, Z., Zhao, W., Peng, J., Fan, J.: Learning deep network for detecting 3D object keypoints and 6D poses. In: CVPR, pp. 14134\u201314142 (2020)","DOI":"10.1109\/CVPR42600.2020.01414"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Liu, L., Yang, L., Chen, W., Gao, X.: Dual-view 3D human pose estimation without camera parameters for action recognition. IET Image Processing. (2021)","DOI":"10.1049\/ipr2.12277"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Tang, R., Wang, L., Guo, Z.: A multi-task neural network for action recognition with 3D key-points. In: ICPR, pp. 3899\u20133906 (2021)","DOI":"10.1109\/ICPR48806.2021.9412348"},{"key":"3_CR11","unstructured":"Paoletti, G., Cavazza, J., Beyan, C., Del Bue, A.: Unsupervised human action recognition with skeletal graph Laplacian and self-supervised viewpoints invariance. BMVC (2021)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Wei, S.E., Simon, T., Kitani, K., Saragih, J.: SimPoE: simulated character control for 3D human pose estimation. In: CVPR, pp. 7159\u20137169 (2021)","DOI":"10.1109\/CVPR46437.2021.00708"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Wandt, B., Rudolph, M., Zell, P., Rhodin, H., Rosenhahn, B.: CanonPose: self-supervised monocular 3D human pose estimation in the wild. In: CVPR, pp. 13294\u201313304 (2021)","DOI":"10.1109\/CVPR46437.2021.01309"},{"key":"3_CR14","unstructured":"Zhang, C., Zhan, F., Chang, Y.: Deep monocular 3D human pose estimation via cascaded dimension-lifting. arXiv preprint arXiv:2104.03520 (2021)"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Wan, C., Probst, T., Gool, L.V., Yao, A.: Self-supervised 3D hand pose estimation through training by fitting. In: CVPR, pp. 10853\u201310862 (2019)","DOI":"10.1109\/CVPR.2019.01111"},{"key":"3_CR16","unstructured":"Li, Y., Torralba, A., Anandkumar, A., Fox, D., Garg, A.: Causal discovery in physical systems from videos. arXiv preprint arXiv:2007.00631 (2020)"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Paoletti, G., Cavazza, J., Beyan, C. and Del Bue, A.: Subspace clustering for action recognition with covariance representations and temporal pruning. In: ICPR, pp. 6035\u20136042 (2021)","DOI":"10.1109\/ICPR48806.2021.9412060"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Shi, R., Xue, Z., You, Y., Lu, C.: Skeleton merger: an unsupervised aligned keypoint detector. In: CVPR, pp. 43\u201352 (2021)","DOI":"10.1109\/CVPR46437.2021.00011"},{"key":"3_CR19","unstructured":"You, Y., Liu, W., Li, Y.L., Wang, W., Lu, C.: UKPGAN: unsupervised keypoint GANeration. arXiv preprint arXiv:2011.11974 (2020)"},{"key":"3_CR20","unstructured":"Bojani\u0107, D., Bartol, K., Petkovi\u0107, T., Pribani\u0107, T.: A review of rigid 3D registration methods. In: 13th International Scientific-Professional Symposium Textile Science and Economy, pp. 286\u2013296 (2020)"},{"key":"3_CR21","unstructured":"Adamczyk, D., Hula, J.: Keypoints selection using evolutionary algorithms. In: ITAT, pp. 186\u2013191 (2020)"},{"key":"3_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1007\/978-3-030-58595-2_33","volume-title":"Computer Vision \u2013 ECCV 2020","author":"C Fernandez-Labrador","year":"2020","unstructured":"Fernandez-Labrador, C., Chhatkuli, A., Paudel, D.P., Guerrero, J.J., Demonceaux, C., Gool, L.V.: Unsupervised learning of category-specific symmetric 3D keypoints from point sets. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 546\u2013563. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58595-2_33"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Jakab, T., Tucker, R., Makadia, A., Wu, J., Snavely, N., Kanazawa, A.: KeypointDeformer: unsupervised 3D keypoint discovery for shape control. In: CVPR, pp. 12783\u201312792 (2021)","DOI":"10.1109\/CVPR46437.2021.01259"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Chen, N., et al.: Unsupervised learning of intrinsic structural representation points. In: CVPR, pp. 9121\u20139130 (2020)","DOI":"10.1109\/CVPR42600.2020.00914"},{"issue":"04","key":"3_CR25","doi-asserted-by":"publisher","first-page":"6422","DOI":"10.1609\/aaai.v34i04.6113","volume":"34","author":"W Wu","year":"2020","unstructured":"Wu, W., Zhang, Y., Wang, D., Lei, Y.: SK-Net: deep learning on point cloud via end-to-end discovery of spatial keypoints. AAAI 34(04), 6422\u20136429 (2020)","journal-title":"AAAI"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Georgakis, G., Karanam, S., Wu, Z., Kosecka, J.: Learning local RGB-to-CAD correspondences for object pose estimation. In: ICCV, pp. 8967\u20138976 (2019)","DOI":"10.1109\/ICCV.2019.00906"},{"key":"3_CR27","unstructured":"You, Y., et al.: Fine-grained object semantic understanding from correspondences. arXiv preprint arXiv:1912.12577 (2019)"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"He, Y., Sun, W., Huang, H., Liu, J., Fan, H., Sun, J.: PVN3D: a deep point-wise 3D keypoints voting network for 6DoF pose estimation. In: CVPR, pp. 11632\u201311641 (2020)","DOI":"10.1109\/CVPR42600.2020.01165"},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: 6-PACK: category-level 6D pose tracker with anchor-based keypoints. In: ICRA, pp. 10059\u201310066 (2020)","DOI":"10.1109\/ICRA40945.2020.9196679"},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Devgon, S., Ichnowski, J., Balakrishna, A., Zhang, H., Goldberg, K.: Orienting novel 3D objects using self-supervised learning of rotation transforms. In: IEEE 16th International Conference on Automation Science and Engineering (CASE), pp. 1453\u20131460 (2020)","DOI":"10.1109\/CASE48305.2020.9217018"},{"key":"3_CR31","unstructured":"Sun, W., et al.: Canonical capsules: unsupervised capsules in canonical pose. arXiv preprint arXiv:2012.04718 (2020)"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Barabanau, I., Artemov, A., Burnaev, E., Murashkin, V.: Monocular 3D object detection via geometric reasoning on keypoints. arXiv preprint arXiv:1905.05618 (2019)","DOI":"10.5220\/0009102506520659"},{"key":"3_CR33","unstructured":"Lu, J., Richter, F., Yip, M.: Robust keypoint detection and pose estimation of robot manipulators with self-occlusions via sim-to-real transfer. arXiv preprint arXiv:2010.08054 (2020)"},{"issue":"6","key":"3_CR34","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381\u2013395 (1981)","journal-title":"Commun. ACM"},{"key":"3_CR35","doi-asserted-by":"crossref","unstructured":"Han, K., et al.: SCNET: learning semantic correspondence. In: ICCV, pp. 1831\u20131840 (2017)","DOI":"10.1109\/ICCV.2017.203"},{"key":"3_CR36","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06430-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:43:28Z","timestamp":1710337408000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06430-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031064296","9783031064302"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06430-2_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lecce","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2021.org\/","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":"Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","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":"168","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":"55% - 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":"4","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)"}}]}}