{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T12:36:29Z","timestamp":1764333389338,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031500688"},{"type":"electronic","value":"9783031500695"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-50069-5_8","type":"book-chapter","created":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T06:02:34Z","timestamp":1705644154000},"page":"81-92","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Hierarchical Edge Aware Learning for\u00a03D Point Cloud"],"prefix":"10.1007","author":[{"given":"Lei","family":"Li","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"8_CR1","unstructured":"Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)"},{"key":"8_CR2","doi-asserted-by":"publisher","first-page":"4436","DOI":"10.1109\/TIP.2021.3072214","volume":"30","author":"S Cheng","year":"2021","unstructured":"Cheng, S., Chen, X., He, X., Liu, Z., Bai, X.: PRA-Net: point relation-aware network for 3D point cloud analysis. IEEE Trans. Image Process. 30, 4436\u20134448 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Dovrat, O., Lang, I., Avidan, S.: Learning to sample. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2760\u20132769 (2019)","DOI":"10.1109\/CVPR.2019.00287"},{"issue":"9","key":"8_CR4","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1109\/83.623193","volume":"6","author":"Y Eldar","year":"1997","unstructured":"Eldar, Y., Lindenbaum, M., Porat, M., Zeevi, Y.Y.: The farthest point strategy for progressive image sampling. IEEE Trans. Image Process. 6(9), 1305\u20131315 (1997)","journal-title":"IEEE Trans. Image Process."},{"key":"8_CR5","doi-asserted-by":"publisher","first-page":"134826","DOI":"10.1109\/ACCESS.2021.3116304","volume":"9","author":"N Engel","year":"2021","unstructured":"Engel, N., Belagiannis, V., Dietmayer, K.: Point transformer. IEEE Access 9, 134826\u2013134840 (2021)","journal-title":"IEEE Access"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Lai, X., et al.: Stratified transformer for 3D point cloud segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8500\u20138509 (2022)","DOI":"10.1109\/CVPR52688.2022.00831"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Lang, I., Manor, A., Avidan, S.: SampleNet: differentiable point cloud sampling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7578\u20137588 (2020)","DOI":"10.1109\/CVPR42600.2020.00760"},{"issue":"1","key":"8_CR8","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1109\/TII.2021.3085669","volume":"18","author":"J Li","year":"2021","unstructured":"Li, J., et al.: Automatic detection and classification system of domestic waste via multimodel cascaded convolutional neural network. IEEE Trans. Industr. Inf. 18(1), 163\u2013173 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"8_CR9","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.cag.2023.08.003","volume":"116","author":"L Li","year":"2023","unstructured":"Li, L., Zhang, T., Kang, Z., Jiang, X.: Mask-FPAN: semi-supervised face parsing in the wild with de-occlusion and UV GAN. Comput. Graph. 116, 185\u2013193 (2023)","journal-title":"Comput. Graph."},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Li, L., Zhang, T., Oehmcke, S., Gieseke, F., Igel, C.: BuildSeg: a general framework for the segmentation of buildings. Nordic Mach. Intell. 2(3) (2022)","DOI":"10.5617\/nmi.10152"},{"key":"8_CR11","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: convolution on X-transformed points. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Lin, Y., Huang, Y., Zhou, S., Jiang, M., Wang, T., Lei, Y.: DA-Net: density-adaptive downsampling network for point cloud classification via end-to-end learning. In: 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), pp. 13\u201318. IEEE (2021)","DOI":"10.1109\/PRAI53619.2021.9551070"},{"issue":"6","key":"8_CR13","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1109\/JAS.2020.1003324","volume":"8","author":"A Muzahid","year":"2020","unstructured":"Muzahid, A., Wan, W., Sohel, F., Wu, L., Hou, L.: CurveNet: curvature-based multitask learning deep networks for 3D object recognition. IEEE\/CAA J. Automatica Sinica 8(6), 1177\u20131187 (2020)","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Oehmcke, S., et al.: Deep learning based 3D point cloud regression for estimating forest biomass. In: Proceedings of the 30th International Conference on Advances in Geographic Information Systems (SIGSPATIAL), pp. 1\u20134. ACM (2022)","DOI":"10.1145\/3557915.3561471"},{"key":"8_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, pp. 652\u2013660 (2017)"},{"key":"8_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: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"8_CR17","unstructured":"Qian, Y., Hou, J., Zhang, Q., Zeng, Y., Kwong, S., He, Y.: MOPS-Net: a matrix optimization-driven network for task-oriented 3D point cloud downsampling. arXiv preprint arXiv:2005.00383 (2020)"},{"issue":"16","key":"8_CR18","doi-asserted-by":"publisher","first-page":"3912","DOI":"10.3390\/rs14163912","volume":"14","author":"JC Revenga","year":"2022","unstructured":"Revenga, J.C., et al.: Above-ground biomass prediction for croplands at a sub-meter resolution using UAV-LiDAR and machine learning methods. Remote Sens. 14(16), 3912 (2022)","journal-title":"Remote Sens."},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Su, H., et al.: SPLATNet: sparse lattice networks for point cloud processing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2530\u20132539 (2018)","DOI":"10.1109\/CVPR.2018.00268"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: KPConv: flexible and deformable convolution for point clouds. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6411\u20136420 (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"issue":"5","key":"8_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. (TOG) 38(5), 1\u201312 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Wiersma, R., Nasikun, A., Eisemann, E., Hildebrandt, K.: DeltaConv: anisotropic point cloud learning with exterior calculus. arXiv preprint arXiv:2111.08799 (2021)","DOI":"10.1145\/3528223.3530166"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Wu, C., Zheng, J., Pfrommer, J., Beyerer, J.: Attention-based point cloud edge sampling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5333\u20135343 (2023)","DOI":"10.1109\/CVPR52729.2023.00516"},{"key":"8_CR24","doi-asserted-by":"crossref","unstructured":"Wu, M., Li, L., Li, H.: FASE: feature-based similarity search on ECG data. In: 2019 IEEE International Conference on Big Knowledge (ICBK), pp. 273\u2013280. IEEE (2019)","DOI":"10.1109\/ICBK.2019.00044"},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., Fuxin, L.: PointConv: deep convolutional networks on 3D point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9621\u20139630 (2019)","DOI":"10.1109\/CVPR.2019.00985"},{"key":"8_CR26","unstructured":"Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912\u20131920 (2015)"},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Xu, M., Ding, R., Zhao, H., Qi, X.: PAConv: position adaptive convolution with dynamic kernel assembling on point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3173\u20133182 (2021)","DOI":"10.1109\/CVPR46437.2021.00319"},{"key":"8_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1007\/978-3-030-01237-3_6","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Xu","year":"2018","unstructured":"Xu, Y., Fan, T., Xu, M., Zeng, L., Qiao, Yu.: SpiderCNN: deep learning on point sets with parameterized convolutional filters. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 90\u2013105. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01237-3_6"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Yan, X., Zheng, C., Li, Z., Wang, S., Cui, S.: PointASNL: robust point clouds processing using nonlocal neural networks with adaptive sampling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5589\u20135598 (2020)","DOI":"10.1109\/CVPR42600.2020.00563"},{"issue":"4","key":"8_CR30","doi-asserted-by":"publisher","first-page":"4250","DOI":"10.1109\/TAES.2023.3238703","volume":"59","author":"T Zhang","year":"2023","unstructured":"Zhang, T., Li, L., Cao, S., Pu, T., Peng, Z.: Attention-guided pyramid context networks for detecting infrared small target under complex background. IEEE Trans. Aerosp. Electron. Syst. 59(4), 4250\u20134261 (2023). https:\/\/doi.org\/10.1109\/TAES.2023.3238703","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"8_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, T., Li, L., Igel, C., Oehmcke, S., Gieseke, F., Peng, Z.: LR-CSNet: low-rank deep unfolding network for image compressive sensing. In: 2022 IEEE 8th International Conference on Computer and Communications (ICCC), pp. 1951\u20131957. IEEE (2022)","DOI":"10.1109\/ICCC56324.2022.10065722"},{"key":"8_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, L., Song, L., Xie, R., Zhang, W.: FACT: fused attention for clothing transfer with generative adversarial networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12894\u201312901 (2020)","DOI":"10.1609\/aaai.v34i07.6987"},{"key":"8_CR33","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Jia, J., Torr, P.H., Koltun, V.: Point transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16259\u201316268 (2021)","DOI":"10.1109\/ICCV48922.2021.01595"},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"Zhou, C., et al.: Multi-scale pseudo labeling for unsupervised deep edge detection (2023). Available at SSRN 4425635","DOI":"10.2139\/ssrn.4425635"}],"container-title":["Lecture Notes in Computer Science","Advances in Computer Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-50069-5_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T06:04:20Z","timestamp":1705644260000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-50069-5_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031500688","9783031500695"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-50069-5_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"20 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Computer Graphics International Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cgi2023","order":10,"name":"conference_id","label":"Conference ID","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"385","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":"149","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}