{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:39:42Z","timestamp":1742938782022,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819770007"},{"type":"electronic","value":"9789819770014"}],"license":[{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"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\/978-981-97-7001-4_8","type":"book-chapter","created":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T18:01:43Z","timestamp":1726941703000},"page":"104-116","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Skeleton-Based Point Cloud Sampling and Its Facilitation to Classification"],"prefix":"10.1007","author":[{"given":"Yugang","family":"Guo","sequence":"first","affiliation":[]},{"given":"Xue-song","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Kuangrong","family":"Hao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,22]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Eldar, Y., Lindenbaum, M., Porat, M., et al.: The farthest point strategy for progressive image sampling. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, Conference B: Computer Vision and Image Processing. (Cat. No. 94CH3440-5), vol. 2, pp. 93\u201397. IEEE (1994)","DOI":"10.1109\/ICPR.1994.577129"},{"key":"8_CR2","volume-title":"Fast Marching Farthest Point Sampling","author":"C Moenning","year":"2003","unstructured":"Moenning, C., Dodgson, N.A.: Fast Marching Farthest Point Sampling. University of Cambridge, Computer Laboratory (2003)"},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Groh, F., Wieschollek, P., Lensch, H.P.A.: Flex-convolution: million-scale point-cloud learning beyond grid-worlds. In: Asian Conference on Computer Vision, pp. 105\u2013122. Springer, Cham (2018)","DOI":"10.1007\/978-3-030-20887-5_7"},{"key":"8_CR4","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"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Lang, I., Manor, A., Samplenet, A.S.: 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"},{"key":"8_CR6","unstructured":"Qian, Y., Hou, J., Zhang, Q., et al.: Mops-net: a matrix optimization-driven network for task-oriented 3D point cloud downsampling. arXiv preprint arXiv:2005.00383 (2020)"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Wu, C., Zheng, J., Pfrommer, J., et al.: 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_CR8","unstructured":"Qi, C.R., Su, H., Mo, K., et al.: 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_CR9","unstructured":"Qi, C.R., Yi, L., Su, H., et al.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"8_CR10","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_CR11","unstructured":"Li, Y., Bu, R., Sun, M., et al.: Pointcnn: convolution on x-transformed points. Adv. Neural Inf. Process. Syst. 31 (2018)"},{"issue":"5","key":"8_CR12","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., et al.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. 38(5), 1\u201312 (2019)","journal-title":"ACM Trans. Graph."},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s41095-021-0229-5","volume":"7","author":"MH Guo","year":"2021","unstructured":"Guo, M.H., Cai, J.X., Liu, Z.N., et al.: PCT: point cloud transformer. Comput. Visual Media 7, 187\u2013199 (2021)","journal-title":"Comput. Visual Media"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Jia, J., et al.: Point transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16259\u201316268 (2021)","DOI":"10.1109\/ICCV48922.2021.01595"},{"issue":"12","key":"8_CR15","doi-asserted-by":"publisher","first-page":"24854","DOI":"10.1109\/TITS.2022.3198836","volume":"23","author":"D Lu","year":"2022","unstructured":"Lu, D., Xie, Q., Gao, K., et al.: 3DCTN: 3D convolution-transformer network for point cloud classification. IEEE Trans. Intell. Transp. Syst. 23(12), 24854\u201324865 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"8_CR16","unstructured":"Blum, H.: A transformation for extracting new descriptions of shape. In: Models for the Perception of Speech and Visual Form, pp. 362\u2013380 (1967)"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Huang, H., Wu, S., Cohen-Or, D., et al.: L1-medial skeleton of point cloud. ACM Trans. Graph. 32(4), 65:1\u201365:8 (2013)","DOI":"10.1145\/2461912.2461913"},{"issue":"6","key":"8_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2816795.2818065","volume":"34","author":"S Wu","year":"2015","unstructured":"Wu, S., Huang, H., Gong, M., et al.: Deep points consolidation. ACM Trans. Graphics 34(6), 1\u201313 (2015)","journal-title":"ACM Trans. Graphics"},{"key":"8_CR19","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Lin, C., Li, C., Liu, Y., et al.: Point2skeleton: learning skeletal representations from point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4277\u20134286 (2021)","DOI":"10.1109\/CVPR46437.2021.00426"},{"key":"8_CR21","unstructured":"Rebain, D., Li, K., Sitzmann, V., et al.: Deep medial fields. arXiv preprint arXiv:2106.03804 (2021)"},{"issue":"2","key":"8_CR22","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1111\/cgf.14484","volume":"41","author":"Z Dou","year":"2022","unstructured":"Dou, Z., Lin, C., Xu, R., et al.: Coverage axis: inner point selection for 3d shape skeletonization. Comput. Graph. Forum 41(2), 419\u2013432 (2022)","journal-title":"Comput. Graph. Forum"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Wen, C., Yu, B., Tao, D.: Learnable Skeleton-aware 3D point cloud sampling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17671\u201317681 (2023)","DOI":"10.1109\/CVPR52729.2023.01695"},{"key":"8_CR24","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s00453-003-1049-y","volume":"38","author":"TK Dey","year":"2004","unstructured":"Dey, T.K., Zhao, W.: Approximating the medial axis from the Voronoi diagram with a convergence guarantee. Algorithmica 38, 179\u2013200 (2004)","journal-title":"Algorithmica"},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Khargonkar, N., Paniagua, B., Vicory, J.: Skeletal point representations with geometric deep learning. arXiv preprint arXiv:2303.02123 (2023)","DOI":"10.1109\/ISBI53787.2023.10230505"},{"key":"8_CR26","unstructured":"Wu, Z., Song, S., Khosla, A., 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":"Lin, Y., Huang, Y., Zhou, S., et al.: 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"}],"container-title":["Communications in Computer and Information Science","Neural Computing for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-7001-4_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T18:02:21Z","timestamp":1726941741000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-7001-4_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,22]]},"ISBN":["9789819770007","9789819770014"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-7001-4_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024,9,22]]},"assertion":[{"value":"22 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Computing for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guilin","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ncaa2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aaci.org.hk\/ncaa2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}