{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T12:26:26Z","timestamp":1726230386266},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031441943"},{"type":"electronic","value":"9783031441950"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44195-0_16","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T12:04:08Z","timestamp":1695297848000},"page":"187-198","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["ROFusion: Efficient Object Detection Using Hybrid Point-Wise Radar-Optical Fusion"],"prefix":"10.1007","author":[{"given":"Liu","family":"Liu","sequence":"first","affiliation":[]},{"given":"Shuaifeng","family":"Zhi","sequence":"additional","affiliation":[]},{"given":"Zhenhua","family":"Du","sequence":"additional","affiliation":[]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xinyu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Huo","sequence":"additional","affiliation":[]},{"given":"Weidong","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.robot.2016.05.001","volume":"83","author":"J \u0106esi\u0107","year":"2016","unstructured":"\u0106esi\u0107, J., Markovi\u0107, I., Cvi\u0161i\u0107, I., Petrovi\u0107, I.: Radar and stereo vision fusion for multitarget tracking on the special euclidean group. Robot. Auton. Syst. 83, 338\u2013348 (2016)","journal-title":"Robot. Auton. Syst."},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3d object detection network for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1907\u20131915 (2017)","DOI":"10.1109\/CVPR.2017.691"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Dreher, M., Er\u00e7elik, E., B\u00e4nziger, T., Knol, A.: Radar-based 2d car detection using deep neural networks. In: Proceedings of the International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1\u20138 (2020)","DOI":"10.1109\/ITSC45102.2020.9294546"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Gaisser, F., Jonker, P.P.: Road user detection with convolutional neural networks: An application to the autonomous shuttle wepod. In Journal of Machine Vision and Applications, pp. 101\u2013104. IEEE (2017)","DOI":"10.23919\/MVA.2017.7986800"},{"key":"16_CR5","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the International Conference on Computer Vision (ICCV), pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Guo, X., Du, J., Gao, J., Wang, W.: Pedestrian detection based on fusion of millimeter wave radar and vision. In: International Conference on Artificial Intelligence and Pattern Recognition, pp. 38\u201342 (2018)","DOI":"10.1145\/3268866.3268868"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Hwang, J.-J., et al.: Cramnet: camera-radar fusion with ray-constrained cross-attention for robust 3d object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 388\u2013405. Springer (2022)","DOI":"10.1007\/978-3-031-19839-7_23"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Kim, Y., Kim, S., Choi, J.W., Kum, D.: Craft: camera-radar 3d object detection with spatio-contextual fusion transformer. arXiv preprint arXiv:2209.06535 (2022)","DOI":"10.1609\/aaai.v37i1.25198"},{"key":"16_CR11","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Liu, J., Xiong, W., Bai, L., Xia, Y., Huang, T., Ouyang, W., Zhu, B.: Deep instance segmentation with automotive radar detection points. IEEE Trans. Intell. Vehicles (2022)","DOI":"10.31219\/osf.io\/k89fb"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Nabati, R., Qi, H.: Centerfusion: center-based radar and camera fusion for 3d object detection. In: Proceedings of the IEEE Workshop on Applications of Computer Vision (WACV), pp. 1527\u20131536 (2021)","DOI":"10.1109\/WACV48630.2021.00157"},{"key":"16_CR15","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 652\u2013660 (2017)"},{"key":"16_CR16","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Neural Information Processing Systems (NIPS), 30 (2017)"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Rebut, J., Ouaknine, A., Malik, W., P\u00e9rez, P.: Raw high-definition radar for multi-task learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17021\u201317030 (2022)","DOI":"10.1109\/CVPR52688.2022.01651"},{"key":"16_CR18","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115, 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"issue":"1","key":"16_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42467-021-00012-z","volume":"3","author":"N Scheiner","year":"2021","unstructured":"Scheiner, N., Kraus, F., Appenrodt, N., Dickmann, J., Sick, B.: Object detection for automotive radar point clouds-a comparison. AI Perspect. 3(1), 1\u201323 (2021)","journal-title":"AI Perspect."},{"key":"16_CR20","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, 30 (2017)"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Vora, S., Lang, A.H., Helou, B., Beijbom, O.: Pointpainting: sequential fusion for 3d object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4604\u20134612 (2020)","DOI":"10.1109\/CVPR42600.2020.00466"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Chen Wang, A., et al.: Densefusion: 6d object pose estimation by iterative dense fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3343\u20133352 (2019)","DOI":"10.1109\/CVPR.2019.00346"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Yang, B., Luo, W., Urtasun, R.: Pixor: real-time 3d object detection from point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7652\u20137660 (2018)","DOI":"10.1109\/CVPR.2018.00798"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Liu, S., Mathew, M., Dubey, A.: Camera radar fusion for increased reliability in adas applications. Electron. Imaging 2018(17), 258-1 (2018)","DOI":"10.2352\/ISSN.2470-1173.2018.17.AVM-258"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44195-0_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T12:06:37Z","timestamp":1695297997000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44195-0_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031441943","9783031441950"],"references-count":24,"URL":"http:\/\/dx.doi.org\/10.1007\/978-3-031-44195-0_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","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":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"type of other papers accepted : 9 Abstract","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}
