{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:49:35Z","timestamp":1777654175454,"version":"3.51.4"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031781124","type":"print"},{"value":"9783031781131","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78113-1_22","type":"book-chapter","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T17:01:35Z","timestamp":1733245295000},"page":"329-345","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["iGrasp: An Interactive 2D-3D Framework for 6-DoF Grasp Detection"],"prefix":"10.1007","author":[{"given":"Jian-Jian","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Ming","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zibo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Lin","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Shi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"22_CR1","unstructured":"W.\u00a0Yuan, A.\u00a0Murali, A.\u00a0Mousavian, and D.\u00a0Fox, \u201cM2T2: multi-task masked transformer for object-centric pick and place,\u201d in Conference on Robot Learning, 2023"},{"key":"22_CR2","unstructured":"H.\u00a0Chen, Y.\u00a0Niu, K.\u00a0Hong, S.\u00a0Liu, Y.\u00a0Wang, Y.\u00a0Li, and K.\u00a0R. Driggs-Campbell, \u201cPredicting object interactions with behavior primitives: An application in stowing tasks,\u201d in Conference on Robot Learning, 2023"},{"key":"22_CR3","unstructured":"H.\u00a0Shi, H.\u00a0Xu, S.\u00a0Clarke, Y.\u00a0Li, and J.\u00a0Wu, \u201cRobocook: Long-horizon elasto-plastic object manipulation with diverse tools,\u201d in Conference on Robot Learning, 2023"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"H.\u00a0Fang, C.\u00a0Wang, M.\u00a0Gou, and C.\u00a0Lu, \u201cGraspnet-1billion: A large-scale benchmark for general object grasping,\u201d in IEEE Conference on Computer Vision and Pattern Recognition, 2020","DOI":"10.1109\/CVPR42600.2020.01146"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"P.\u00a0Ni, W.\u00a0Zhang, X.\u00a0Zhu, and Q.\u00a0Cao, \u201cPointnet++ grasping: Learning an end-to-end spatial grasp generation algorithm from sparse point clouds,\u201d in IEEE International Conference on Robotics and Automation, 2020","DOI":"10.1109\/ICRA40945.2020.9196740"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"C.\u00a0Wang, H.\u00a0Fang, M.\u00a0Gou, H.\u00a0Fang, J.\u00a0Gao, and C.\u00a0Lu, \u201cGraspness discovery in clutters for fast and accurate grasp detection,\u201d in IEEE International Conference on Computer Vision, 2021","DOI":"10.1109\/ICCV48922.2021.01566"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Li, T.\u00a0Kong, R.\u00a0Chu, Y.\u00a0Li, P.\u00a0Wang, and L.\u00a0Li, \u201cSimultaneous semantic and collision learning for 6-dof grasp pose estimation,\u201d in IEEE International Conference on Intelligent Robots and Systems, 2021","DOI":"10.1109\/IROS51168.2021.9636012"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"D.\u00a0Hoang, J.\u00a0A. Stork, and T.\u00a0Stoyanov, \u201cContext-aware grasp generation in cluttered scenes,\u201d in IEEE International Conference on Robotics and Automation, 2022","DOI":"10.1109\/ICRA46639.2022.9811371"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Liu, Z.\u00a0Chen, S.\u00a0Xie, and W.\u00a0Zheng, \u201cTransgrasp: A multi-scale hierarchical point transformer for 7-dof grasp detection,\u201d in IEEE International Conference on Robotics and Automation, 2022","DOI":"10.1109\/ICRA46639.2022.9812001"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"X.\u00a0Wu, J.\u00a0Cai, J.\u00a0Jiang, D.\u00a0Zheng, Y.\u00a0Wei, and W.\u00a0Zheng, \u201cAn economic framework for 6-dof grasp detection,\u201d in European Conference on Computer Vision, 2024","DOI":"10.1007\/978-3-031-73383-3_21"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"X.\u00a0Liu, Y.\u00a0Zhang, H.\u00a0Cao, D.\u00a0Shan, and J.\u00a0Zhao, \u201cJoint segmentation and grasp pose detection with multi-modal feature fusion network,\u201d in IEEE International Conference on Robotics and Automation, 2023","DOI":"10.1109\/ICRA48891.2023.10160253"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"S.\u00a0Chen, W.\u00a0Tang, P.\u00a0Xie, W.\u00a0Yang, and G.\u00a0Wang, \u201cEfficient heatmap-guided 6-dof grasp detection in cluttered scenes,\u201d IEEE Robotics and Automation Letters, 2023","DOI":"10.1109\/LRA.2023.3290513"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"M.\u00a0Sundermeyer, A.\u00a0Mousavian, R.\u00a0Triebel, and D.\u00a0Fox, \u201cContact-graspnet: Efficient 6-dof grasp generation in cluttered scenes,\u201d in IEEE International Conference on Robotics and Automation, 2021","DOI":"10.1109\/ICRA48506.2021.9561877"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"M.\u00a0Gou, H.\u00a0Fang, Z.\u00a0Zhu, S.\u00a0Xu, C.\u00a0Wang, and C.\u00a0Lu, \u201cRGB matters: Learning 7-dof grasp poses on monocular RGBD images,\u201d in IEEE International Conference on Robotics and Automation, 2021","DOI":"10.1109\/ICRA48506.2021.9561409"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Shi, Z.\u00a0Tang, X.\u00a0Cai, H.\u00a0Zhang, D.\u00a0Hu, and X.\u00a0Xu, \u201cSymmetrygrasp: Symmetry-aware antipodal grasp detection from single-view RGB-D images,\u201d IEEE Robotics and Automation Letters, 2022","DOI":"10.1109\/LRA.2022.3214785"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"M.\u00a0A. Roa and R.\u00a0Su\u00e1rez, \u201cComputation of independent contact regions for grasping 3-d objects,\u201d IEEE Transactions on Robotics, 2009","DOI":"10.1109\/TRO.2009.2020351"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"X.\u00a0Deng, Y.\u00a0Xiang, A.\u00a0Mousavian, C.\u00a0Eppner, T.\u00a0Bretl, and D.\u00a0Fox, \u201cSelf-supervised 6d object pose estimation for robot manipulation,\u201d in IEEE International Conference on Robotics and Automation, 2020","DOI":"10.1109\/ICRA40945.2020.9196714"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"A.\u00a0ten Pas, M.\u00a0Gualtieri, K.\u00a0Saenko, and R.\u00a0P. Jr., \u201cGrasp pose detection in point clouds,\u201d The International Journal of Robotics Research, 2017","DOI":"10.1177\/0278364917735594"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"H.\u00a0Liang, X.\u00a0Ma, S.\u00a0Li, M.\u00a0G\u00f6rner, S.\u00a0Tang, B.\u00a0Fang, F.\u00a0Sun, and J.\u00a0Zhang, \u201cPointnetgpd: Detecting grasp configurations from point sets,\u201d in IEEE International Conference on Robotics and Automation, 2019","DOI":"10.1109\/ICRA.2019.8794435"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"A.\u00a0Mousavian, C.\u00a0Eppner, and D.\u00a0Fox, \u201c6-dof graspnet: Variational grasp generation for object manipulation,\u201d in IEEE International Conference on Computer Vision, 2019","DOI":"10.1109\/ICCV.2019.00299"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"M.\u00a0Sandler, A.\u00a0G. Howard, M.\u00a0Zhu, A.\u00a0Zhmoginov, and L.\u00a0Chen, \u201cMobilenetv2: Inverted residuals and linear bottlenecks,\u201d in IEEE Conference on Computer Vision and Pattern Recognition, 2018","DOI":"10.1109\/CVPR.2018.00474"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"X.\u00a0Zhang, X.\u00a0Zhou, M.\u00a0Lin, and J.\u00a0Sun, \u201cShufflenet: An extremely efficient convolutional neural network for mobile devices,\u201d in IEEE Conference on Computer Vision and Pattern Recognition, 2018","DOI":"10.1109\/CVPR.2018.00716"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"M.\u00a0Tan, B.\u00a0Chen, R.\u00a0Pang, V.\u00a0Vasudevan, M.\u00a0Sandler, A.\u00a0Howard, and Q.\u00a0V. Le, \u201cMnasnet: Platform-aware neural architecture search for mobile,\u201d in IEEE Conference on Computer Vision and Pattern Recognition, 2019","DOI":"10.1109\/CVPR.2019.00293"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"B.\u00a0Zhao, H.\u00a0Zhang, X.\u00a0Lan, H.\u00a0Wang, Z.\u00a0Tian, and N.\u00a0Zheng, \u201cRegnet: Region-based grasp network for end-to-end grasp detection in point clouds,\u201d in IEEE International Conference on Robotics and Automation, 2021","DOI":"10.1109\/ICRA48506.2021.9561920"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"J.\u00a0Mahler, J.\u00a0Liang, S.\u00a0Niyaz, M.\u00a0Laskey, R.\u00a0Doan, X.\u00a0Liu, J.\u00a0A. Ojea, and K.\u00a0Goldberg, \u201cDex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics,\u201d in Robotics: Science and Systems, 2017","DOI":"10.15607\/RSS.2017.XIII.058"},{"key":"22_CR26","doi-asserted-by":"crossref","unstructured":"C.\u00a0B. Choy, J.\u00a0Gwak, and S.\u00a0Savarese, \u201c4d spatio-temporal convnets: Minkowski convolutional neural networks,\u201d in IEEE Conference on Computer Vision and Pattern Recognition, 2019","DOI":"10.1109\/CVPR.2019.00319"},{"key":"22_CR27","unstructured":"A.\u00a0Paszke, S.\u00a0Gross, F.\u00a0Massa, A.\u00a0Lerer, J.\u00a0Bradbury, G.\u00a0Chanan, T.\u00a0Killeen, Z.\u00a0Lin, N.\u00a0Gimelshein, L.\u00a0Antiga, A.\u00a0Desmaison, A.\u00a0K\u00f6pf, E.\u00a0Z. Yang, Z.\u00a0DeVito, M.\u00a0Raison, A.\u00a0Tejani, S.\u00a0Chilamkurthy, B.\u00a0Steiner, L.\u00a0Fang, J.\u00a0Bai, and S.\u00a0Chintala, \u201cPytorch: An imperative style, high-performance deep learning library,\u201d in Advances in Neural Information Processing Systems, 2019"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78113-1_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T17:06:47Z","timestamp":1733245607000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78113-1_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"ISBN":["9783031781124","9783031781131"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78113-1_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"4 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","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":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}