{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:55:47Z","timestamp":1743083747966,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811692468"},{"type":"electronic","value":"9789811692475"}],"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\/978-981-16-9247-5_30","type":"book-chapter","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T21:25:33Z","timestamp":1641936333000},"page":"389-402","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["3D Grasping Pose Detection Method Based on Improved PointNet Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8980-4528","authenticated-orcid":false,"given":"Jiahui","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunhan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haotian","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4845-0097","authenticated-orcid":false,"given":"Huasong","family":"Min","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"30_CR1","doi-asserted-by":"crossref","unstructured":"Schaub, H., Schttl, A.: 6-DOF Grasp detection for unknown objects. In: ACIT 2020 (10th International Conference on Advanced Computer Information Technologies). IEEE(2020)","DOI":"10.1109\/ACIT49673.2020.9208918"},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Przybylski, M., Asfour, T., Dillmann, R.: Planning grasps for robotic hands using a novel object representation based on the medial axis transform. In: 2011 IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 1781\u20131788. IEEE (2011)","DOI":"10.1109\/IROS.2011.6094937"},{"key":"30_CR3","unstructured":"Bohg, J., Kragic, D.: Grasping familiar objects using shape context. In: 2009 International Conference on Advanced Robotics, pp. 1\u20136. IEEE (2009)"},{"issue":"4","key":"30_CR4","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.robot.2009.10.003","volume":"58","author":"J Bohg","year":"2010","unstructured":"Bohg, J., Kragic, D.: Learning grasping points with shape context. Robot. Auton. Syst. 58(4), 362\u2013377 (2010)","journal-title":"Robot. Auton. Syst."},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. Int. J. Robot. Res. 34(4\u20135) (2013)","DOI":"10.1177\/0278364914549607"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Redmon, J., Angelova, A.: Real-Time Grasp detection using convolutional neural networks. In: Proceedings IEEE International Conference on Robotics & Automation (2014)","DOI":"10.1109\/ICRA.2015.7139361"},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Mahler, J., Liang J., Niyaz, S., et al.: Dex-Net 2.0: deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics (2017)","DOI":"10.15607\/RSS.2017.XIII.058"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Savarese, S., Li, F. F.: 3D generic object categorization, localization and pose estimation. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, Rio de Janeiro, Brazil, October, pp. 14\u201320. IEEE (2007)","DOI":"10.1109\/ICCV.2007.4408987"},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Zeng, A., Yu, K.T., Song, S., et al.: Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2017)","DOI":"10.1109\/ICRA.2017.7989165"},{"key":"30_CR10","doi-asserted-by":"crossref","unstructured":"Varley, J., Dechant C., Richardson, A., et al.: Shape Completion Enabled Robotic Grasping. IEEE (2017)","DOI":"10.1109\/IROS.2017.8206060"},{"key":"30_CR11","unstructured":"Qi, C. R., Su, H., Mo, K., et al.: PointNet: deep learning on point sets for 3D classification and segmentation. In: The 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.77\u201385. IEEE (2017)"},{"issue":"13\u201314","key":"30_CR12","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1177\/0278364917735594","volume":"36","author":"A Ten Pas","year":"2017","unstructured":"Ten Pas, A., Gualtieri, M., Saenko, K., et al.: Grasp pose detection in point clouds. Int. J. Robot. Res. 36(13\u201314), 1455\u20131473 (2017)","journal-title":"Int. J. Robot. Res."},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Liang, H., Ma, X., Li, S., et al.: PointNetGPD: Detecting grasp configurations from point sets (2018)","DOI":"10.1109\/ICRA.2019.8794435"},{"key":"30_CR14","unstructured":"Ramachandran, P., Zoph, B., Le. Q.V.: Searching for activation functions (2017)"},{"key":"30_CR15","unstructured":"Misra, D.: Mish: A self regularized non-monotonic neural activation function. arXiv Preprint (4). arXiv:1908.08681(2019)"},{"key":"30_CR16","unstructured":"Liu, W., Wen, Y., Yu, Z., et al.: Large-margin softmax loss for convolutional neural networks. In: ICML, vol. 2, no.3, p. 7 (2016)"},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Siciliano, Bruno, Khatib, et al.: Springer handbook of robotics. Springer-Verlag, New York, Inc. (2007)","DOI":"10.1007\/978-3-540-30301-5"},{"key":"30_CR18","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. JMLR.org (2015)"},{"key":"30_CR19","unstructured":"Kingma, D., Ba J.: Adam: a method for stochastic optimization. Computer Science (2014)"}],"container-title":["Communications in Computer and Information Science","Cognitive Systems and Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-9247-5_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T03:00:43Z","timestamp":1726455643000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-9247-5_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811692468","9789811692475"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-9247-5_30","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Cognitive Systems and Signal Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Suzhou","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iccsip2021.tsingzhan.com\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"105","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":"41","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":"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":"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":"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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}