{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T08:38:17Z","timestamp":1771922297541,"version":"3.50.1"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200762","type":"print"},{"value":"9783031200779","type":"electronic"}],"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\/978-3-031-20077-9_40","type":"book-chapter","created":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T16:21:52Z","timestamp":1667665312000},"page":"684-700","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Label-Guided Auxiliary Training Improves 3D Object Detector"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8195-4978","authenticated-orcid":false,"given":"Yaomin","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7649-8085","authenticated-orcid":false,"given":"Xinmei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5126-838X","authenticated-orcid":false,"given":"Yichen","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2879-3244","authenticated-orcid":false,"given":"Zhiyuan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9389-6472","authenticated-orcid":false,"given":"Chaomin","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6818-1125","authenticated-orcid":false,"given":"Zhengping","family":"Che","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guixu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2983-555X","authenticated-orcid":false,"given":"Yaxin","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feifei","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4418-0114","authenticated-orcid":false,"given":"Jian","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,6]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Chen, J., Lei, B., Song, Q., Ying, H., Chen, D.Z., Wu, J.: A hierarchical graph network for 3D object detection on point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 392\u2013401 (2020)","DOI":"10.1109\/CVPR42600.2020.00047"},{"key":"40_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Q., Sun, L., Wang, Z., Jia, K., Yuille, A.: Object as hotspots: an anchor-free 3D object detection approach via firing of hotspots. In: European Conference on Computer Vision, pp. 68\u201384. Springer (2020)","DOI":"10.1007\/978-3-030-58589-1_5"},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Cheng, B., Sheng, L., Shi, S., Yang, M., Xu, D.: Back-tracing representative points for voting-based 3D object detection in point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8963\u20138972 (2021)","DOI":"10.1109\/CVPR46437.2021.00885"},{"key":"40_CR4","unstructured":"Chong, Z., Ma, X., Zhang, H., Yue, Y., Li, H., Wang, Z., Ouyang, W.: Monodistill: learning spatial features for monocular 3d object detection. In: International Conference on Learning Representations (2022)"},{"key":"40_CR5","unstructured":"Contributors, M.: MMDetection3D: OpenMMLab next-generation platform for general 3D object detection (2020). https:\/\/github.com\/open-mmlab\/mmdetection3d"},{"key":"40_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/978-3-030-58595-2_32","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Hao","year":"2020","unstructured":"Hao, M., Liu, Y., Zhang, X., Sun, J.: LabelEnc: A New Intermediate Supervision Method for Object Detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 529\u2013545. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58595-2_32"},{"key":"40_CR7","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.H., Friedman, J.H.: The elements of statistical learning: data mining, inference, and prediction, vol. 2. Springer (2009)","DOI":"10.1007\/978-0-387-84858-7"},{"key":"40_CR8","doi-asserted-by":"crossref","unstructured":"He, C., Zeng, H., Huang, J., Hua, X.S., Zhang, L.: Structure aware single-stage 3D object detection from point cloud. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11873\u201311882 (2020)","DOI":"10.1109\/CVPR42600.2020.01189"},{"key":"40_CR9","unstructured":"Hinton, G.E., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. CoRR abs\/1503.02531 (2015)"},{"key":"40_CR10","first-page":"16468","volume":"34","author":"Z Kang","year":"2021","unstructured":"Kang, Z., Zhang, P., Zhang, X., Sun, J., Zheng, N.: Instance-conditional knowledge distillation for object detection. Adv. Neural. Inf. Process. Syst. 34, 16468\u201316480 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: Pointpillars: fast encoders for object detection from point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.01298"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, K., Liu, C., Qin, Z., Luo, Z., Wang, J.: Structured knowledge distillation for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2604\u20132613 (2019)","DOI":"10.1109\/CVPR.2019.00271"},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhang, Z., Cao, Y., Hu, H., Tong, X.: Group-free 3D object detection via transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2949\u20132958 (2021)","DOI":"10.1109\/ICCV48922.2021.00294"},{"key":"40_CR14","doi-asserted-by":"crossref","unstructured":"Misra, I., Girdhar, R., Joulin, A.: An end-to-end transformer model for 3D object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2906\u20132917 (2021)","DOI":"10.1109\/ICCV48922.2021.00290"},{"key":"40_CR15","doi-asserted-by":"crossref","unstructured":"Mostajabi, M., Maire, M., Shakhnarovich, G.: Regularizing deep networks by modeling and predicting label structure. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5629\u20135638 (2018)","DOI":"10.1109\/CVPR.2018.00590"},{"key":"40_CR16","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Chen, X., Litany, O., Guibas, L.J.: Imvotenet: boosting 3D object detection in point clouds with image votes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4404\u20134413 (2020)","DOI":"10.1109\/CVPR42600.2020.00446"},{"key":"40_CR17","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Litany, O., He, K., Guibas, L.J.: Deep Hough voting for 3D object detection in point clouds. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9277\u20139286 (2019)","DOI":"10.1109\/ICCV.2019.00937"},{"key":"40_CR18","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum pointnets for 3D object detection from RGB-D data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 918\u2013927 (2018)","DOI":"10.1109\/CVPR.2018.00102"},{"key":"40_CR19","doi-asserted-by":"crossref","unstructured":"Qian, R., Lai, X., Li, X.: 3d object detection for autonomous driving: a survey. Pattern Recognition, p. 108796 (2022)","DOI":"10.1016\/j.patcog.2022.108796"},{"key":"40_CR20","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)"},{"key":"40_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\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4604\u20134612 (2020)","DOI":"10.1109\/CVPR42600.2020.00466"},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"Wang, C., Ma, C., Zhu, M., Yang, X.: PointAugmenting: cross-modal augmentation for 3D object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11794\u201311803 (2021)","DOI":"10.1109\/CVPR46437.2021.01162"},{"key":"40_CR23","unstructured":"Wang, Y., Fathi, A., Wu, J., Funkhouser, T., Solomon, J.: Multi-frame to single-frame: Knowledge distillation for 3d object detection. In: The Workshop on Perception for Autonomous Driving at the European Conference on Computer Vision (2020)"},{"key":"40_CR24","unstructured":"Wang, Y., Guizilini, V.C., Zhang, T., Wang, Y., Zhao, H., Solomon, J.: DETR3D: 3D object detection from multi-view images via 3D-to-2D queries. In: Conference on Robot Learning, pp. 180\u2013191. PMLR (2022)"},{"key":"40_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Multi-stage fusion for multi-class 3d lidar detection. In: IEEE\/CVF International Conference on Computer Vision Workshops, ICCVW 2021, pp. 3113\u20133121 (2021)","DOI":"10.1109\/ICCVW54120.2021.00347"},{"key":"40_CR26","doi-asserted-by":"crossref","unstructured":"Xu, Q., Zhou, Y., Wang, W., Qi, C.R., Anguelov, D.: SPG: unsupervised domain adaptation for 3D object detection via semantic point generation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision pp. 15446\u201315456 (2021)","DOI":"10.1109\/ICCV48922.2021.01516"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Yang, Z., Sun, Y., Liu, S., Jia, J.: 3DSSD: Point-based 3D single stage object detector. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 11040\u201311048 (2020)","DOI":"10.1109\/CVPR42600.2020.01105"},{"key":"40_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1007\/978-3-030-58583-9_43","volume-title":"Computer Vision \u2013 ECCV 2020","author":"JH Yoo","year":"2020","unstructured":"Yoo, J.H., Kim, Y., Kim, J., Choi, J.W.: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-view Spatial Feature Fusion for 3D Object Detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12372, pp. 720\u2013736. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58583-9_43"},{"key":"40_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, L., Chen, X., Dong, R., Ma, K.: Region-aware knowledge distillation for efficient image-to-image translation. arXiv preprint arXiv:2205.12451 (2022)","DOI":"10.1109\/CVPR52688.2022.01214"},{"key":"40_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, L., Chen, X., Tu, X., Wan, P., Xu, N., Ma, K.: Wavelet knowledge distillation: towards efficient image-to-image translation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12464\u201312474 (2022)","DOI":"10.1109\/CVPR52688.2022.01214"},{"key":"40_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, L., Dong, R., Tai, H.S., Ma, K.: Pointdistiller: structured knowledge distillation towards efficient and compact 3d detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52729.2023.02087"},{"key":"40_CR32","unstructured":"Zhang, L., Ma, K.: Improve object detection with feature-based knowledge distillation: towards accurate and efficient detectors. In: International Conference on Learning Representations (2020)"},{"key":"40_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yu, M., Chen, T., Shi, Z., Bao, C., Ma, K.: Auxiliary training: towards accurate and robust models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 372\u2013381 (2020)","DOI":"10.1109\/CVPR42600.2020.00045"},{"key":"40_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, P., Kang, Z., Yang, T., Zhang, X., Zheng, N., Sun, J.: Lgd: label-guided self-distillation for object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 3309\u20133317 (2022)","DOI":"10.1609\/aaai.v36i3.20240"},{"key":"40_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-030-58610-2_19","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Zhang","year":"2020","unstructured":"Zhang, Z., Sun, B., Yang, H., Huang, Q.: H3DNet: 3D object detection using hybrid geometric primitives. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. H3DNet: 3D object detection using hybrid geometric primitives, vol. 12357, pp. 311\u2013329. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_19"},{"key":"40_CR36","doi-asserted-by":"crossref","unstructured":"Zheng, W., Tang, W., Jiang, L., Fu, C.W.: SE-SSD: self-ensembling single-stage object detector from point cloud. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14494\u201314503 (2021)","DOI":"10.1109\/CVPR46437.2021.01426"},{"key":"40_CR37","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Wang, Y.: Student customized knowledge distillation: bridging the gap between student and teacher. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5057\u20135066 (2021)","DOI":"10.1109\/ICCV48922.2021.00501"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20077-9_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T05:11:24Z","timestamp":1701321084000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20077-9_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200762","9783031200779"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20077-9_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"6 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","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 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}