{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T13:14:09Z","timestamp":1726060449590},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030364014"},{"type":"electronic","value":"9783030364021"}],"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\/978-3-030-36402-1_29","type":"book-chapter","created":{"date-parts":[[2019,11,28]],"date-time":"2019-11-28T03:04:01Z","timestamp":1574910241000},"page":"271-278","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Optimized PointNet for 3D Object Classification"],"prefix":"10.1007","author":[{"given":"Zhuangzhuang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenmei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guan","family":"Gui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,11,29]]},"reference":[{"issue":"2","key":"29_CR1","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/MCG.2016.36","volume":"36","author":"K Yanaka","year":"2016","unstructured":"Yanaka, K., Yamanouchi, T.: 3D image display courses for information media students. IEEE Comput. Graph. Appl. 36(2), 68\u201373 (2016)","journal-title":"IEEE Comput. Graph. Appl."},{"key":"29_CR2","unstructured":"Grubisic, I., Gjenero, L., Lipic, T., Sovic, I., Skala, T.: Active 3D scanning based 3D thermography system and medical applications. In: Proceedings of the 34th International Convention MIPRO, pp. 269\u2013273 (2011)"},{"issue":"1","key":"29_CR3","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/JBHI.2016.2636665","volume":"21","author":"D Ravi","year":"2016","unstructured":"Ravi, D., et al.: Deep learning for health informatics. IEEE J. Biomed. Heal. Inform. 21(1), 4\u201321 (2016)","journal-title":"IEEE J. Biomed. Heal. Inform."},{"issue":"9","key":"29_CR4","doi-asserted-by":"publisher","first-page":"8549","DOI":"10.1109\/TVT.2018.2851783","volume":"67","author":"H Huang","year":"2018","unstructured":"Huang, H., Yang, J., Song, Y., Huang, H., Gui, G.: Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system. IEEE Trans. Veh. Technol. 67(9), 8549\u20138560 (2018)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"29_CR5","doi-asserted-by":"publisher","first-page":"67940","DOI":"10.1109\/ACCESS.2018.2879324","volume":"6","author":"L Zhang","year":"2018","unstructured":"Zhang, L., Jia, J., Gui, G., Hao, X., Gao, W., Wang, M.: Deep learning based improved classification system for designing tomato harvesting robot. IEEE Access 6, 67940\u201367950 (2018)","journal-title":"IEEE Access"},{"key":"29_CR6","doi-asserted-by":"publisher","first-page":"36274","DOI":"10.1109\/ACCESS.2019.2903127","volume":"7","author":"W Li","year":"2019","unstructured":"Li, W., Liu, H., Wang, Y., Li, Z., Jia, Y., Gui, G.: Deep learning-based classification methods for remote sensing images in urban built-up areas. IEEE Access 7, 36274\u201336284 (2019)","journal-title":"IEEE Access"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Gomez-Donoso, F., Garcia-Rodriguez, J., Orts-Escolano, S., Cazorla, M., Azorin-Lopez, J.: PointNet: a 3D convolutional neural network for real-time object class recognition. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2016, pp. 1578\u20131584, October 2016","DOI":"10.1109\/IJCNN.2016.7727386"},{"key":"29_CR8","unstructured":"Wang, X., Liu, M.: Multi-view deep metric learning for volumetric image recognition. In: Proceedings - IEEE International Conference on Multimedia and Expo, Mvdml, 2018, vol. 2018, pp. 1\u20136, July 2018"},{"key":"29_CR9","unstructured":"Gao, Y., Radha, H.: Multi-view image coding using 3-D voxel models. In: Proceedings - International Conference on Image Processing, ICIP, vol. 2, pp. 257\u2013260 (2005)"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Ge, L., Cai, Y., Weng, J., Yuan, J.: Hand PointNet: 3D hand pose estimation using point sets. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 8417\u20138426 (2018)","DOI":"10.1109\/CVPR.2018.00878"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Li, Z.: A discriminative learning convolutional neural network for facial expression recognition. In: 2017 3rd IEEE International Conference on Computer and Communications, pp. 1641\u20131646 (2017)","DOI":"10.1109\/CompComm.2017.8322818"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Ge, L., Liang, H., Yuan, J., Thalmann, D.: Robust 3d hand pose estimation in single depth images: from single-view cnn to multi-view CNNs. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016, pp. 3593\u20133601 (2016)","DOI":"10.1109\/CVPR.2016.391"},{"key":"29_CR13","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017, pp. 77\u201385, January 2017"},{"key":"29_CR14","unstructured":"Chen, Y., Chen, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. In: International Conference on Neural Information Processing Systems, pp. 1988\u20131996 (2014)"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07\u201312 June, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Advanced Hybrid Information Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-36402-1_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,28]],"date-time":"2019-11-28T03:21:40Z","timestamp":1574911300000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-36402-1_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030364014","9783030364021"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-36402-1_29","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"29 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADHIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Hybrid Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"21 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adhip2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/adhip.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":"EAI compass","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"237","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":"101","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":"43% - 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":"4","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":"2","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}