{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T21:11:55Z","timestamp":1785013915264,"version":"3.55.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"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":["Vis Comput"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00371-024-03555-4","type":"journal-article","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T12:01:41Z","timestamp":1721131301000},"page":"2629-2638","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Point cloud downsampling based on the transformer features"],"prefix":"10.1007","volume":"41","author":[{"given":"Alireza","family":"Dehghanpour","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Sharifi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9473-5459","authenticated-orcid":false,"given":"Masoud","family":"Dehyadegari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,16]]},"reference":[{"issue":"5","key":"3555_CR1","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1007\/s00371-021-02103-8","volume":"38","author":"HT Li","year":"2022","unstructured":"Li, H.T., Todd, Z., Bielski, N., Carroll, F.: 3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation. Vis. Comput. 38(5), 1759\u20131774 (2022)","journal-title":"Vis. Comput."},{"key":"3555_CR2","doi-asserted-by":"crossref","unstructured":"Nezhadarya, E., Taghavi, E., Razani, R., Liu, B., Luo, J.: Adaptive hierarchical down-sampling for point cloud classification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 12956\u201312964 (2020)","DOI":"10.1109\/CVPR42600.2020.01297"},{"key":"3555_CR3","doi-asserted-by":"crossref","unstructured":"Wang, X., Jin, Y., Cen, Y., Lang, C., Li, Y.: PST-NET: point cloud sampling via point-based transformer. In: Image and graphics: 11th international conference, ICIG 2021, Haikou, China, August 6\u20138, 2021, Proceedings, Part III 11, Springer, pp. 57\u201369 (2021)","DOI":"10.1007\/978-3-030-87361-5_5"},{"key":"3555_CR4","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"3555_CR5","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":"3555_CR6","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"},{"key":"3555_CR7","unstructured":"Qian, Y., Hou, J., Zhang, Q., Zeng, Y., Kwong, S., He, Y.: MOPS-Net: a matrix optimization-driven network fortask-oriented 3D point cloud downsampling. arXiv:2005.00383 (2020)"},{"key":"3555_CR8","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":"3555_CR9","doi-asserted-by":"crossref","unstructured":"Prokudin, S., Lassner, C., Romero, J.: Efficient learning on point clouds with basis point sets. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 4332\u20134341 (2019)","DOI":"10.1109\/ICCV.2019.00443"},{"key":"3555_CR10","doi-asserted-by":"crossref","unstructured":"Meng, H.-Y., Gao, L., Lai, Y.-K., Manocha, D.: VV-Net: Voxel VAE Net with group convolutions for point cloud segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 8500\u20138508 (2019)","DOI":"10.1109\/ICCV.2019.00859"},{"key":"3555_CR11","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Tuzel, O.: VoxelNet: end-to-end learning for point cloud based 3D object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4490\u20134499 (2018)","DOI":"10.1109\/CVPR.2018.00472"},{"issue":"5","key":"3555_CR12","doi-asserted-by":"publisher","first-page":"213","DOI":"10.3390\/ijgi8050213","volume":"8","author":"F Poux","year":"2019","unstructured":"Poux, F., Billen, R.: Voxel-based 3D point cloud semantic segmentation: unsupervised geometric and relationship featuring vs deep learning methods. ISPRS Int. J. Geo Inf. 8(5), 213 (2019)","journal-title":"ISPRS Int. J. Geo Inf."},{"key":"3555_CR13","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s41095-021-0229-5","volume":"7","author":"M-H Guo","year":"2021","unstructured":"Guo, M.-H., Cai, J.-X., Liu, Z.-N., Mu, T.-J., Martin, R.R., Hu, S.-M.: Pct: point cloud transformer. Comput. Vis. Media 7, 187\u2013199 (2021)","journal-title":"Comput. Vis. Media"},{"key":"3555_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108769","volume":"247","author":"G Wang","year":"2022","unstructured":"Wang, G., Zhai, Q., Liu, H.: Cross self-attention network for 3D point cloud. Knowl.-Based Syst. 247, 108769 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"3555_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, C., Wan, H., Shen, X., Wu, Z.: PatchFormer: an efficient point transformer with patch attention. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 11799\u201311808 (2022)","DOI":"10.1109\/CVPR52688.2022.01150"},{"key":"3555_CR16","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":"3555_CR17","doi-asserted-by":"crossref","unstructured":"Zhong, Q., Han, X.-F.: Point cloud learning with transformer. arXiv:2104.13636 (2021)","DOI":"10.21203\/rs.3.rs-2200447\/v1"},{"issue":"3","key":"3555_CR18","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1007\/s00371-021-02351-8","volume":"39","author":"L Chen","year":"2023","unstructured":"Chen, L., Zhang, Q.: DDGCN: graph convolution network based on direction and distance for point cloud learning. Vis. Comput. 39(3), 863\u2013873 (2023)","journal-title":"Vis. Comput."},{"key":"3555_CR19","doi-asserted-by":"crossref","unstructured":"Wu, P., Gu, L., Yan, X., Xie, H., Wang, F.L., Cheng, G., Wei, M.: PV-RCNN++: semantical point-voxel feature interaction for 3D object detection. Vis. Comput. 39(6), 2425\u20132440 (2023)","DOI":"10.1007\/s00371-022-02672-2"},{"key":"3555_CR20","doi-asserted-by":"crossref","unstructured":"Wu, S., Tao, J., Wu, C., Chen, L.: Low-overlap point cloud registration algorithm based on coupled iteration. Vis. Comput. 40(5), 1\u201312 (2023)","DOI":"10.1007\/s00371-023-03016-4"},{"key":"3555_CR21","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., Markham, A.: RandLA-Net: efficient semantic segmentation of large-scale point clouds. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 11108\u201311117 (2020)","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"3555_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2022.103639","volume":"89","author":"Y Yang","year":"2022","unstructured":"Yang, Y., Wang, A., Bu, D., Feng, Z., Liang, J.: As-Net: an attention-aware downsampling network for point clouds oriented to classification tasks. J. Vis. Commun. Image Represent. 89, 103639 (2022)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"3555_CR23","doi-asserted-by":"crossref","unstructured":"Zhao, T., Zeng, H., Zhang, B., Fan, B., Li, C.: Point-voxel dual stream transformer for 3D point cloud learning. Vis. Comput. 1\u201317 (2023)","DOI":"10.1007\/s00371-023-03107-2"},{"key":"3555_CR24","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"3555_CR25","doi-asserted-by":"crossref","unstructured":"Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q.V., Salakhutdinov, R.: Transformer-xl: attentive language models beyond a fixed-length context. arXiv:1901.02860 (2019)","DOI":"10.18653\/v1\/P19-1285"},{"key":"3555_CR26","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929 (2020)"},{"key":"3555_CR27","doi-asserted-by":"crossref","unstructured":"Hu, H., Zhang, Z., Xie, Z., Lin, S.: Local relation networks for image recognition. In: Proceedings of the IEEE\/CVF international conference on computer Vision, pp. 3464\u20133473 (2019)","DOI":"10.1109\/ICCV.2019.00356"},{"key":"3555_CR28","unstructured":"Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3D ShapeNets: a deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1912\u20131920 (2015)"},{"issue":"6","key":"3555_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2980179.2980238","volume":"35","author":"L Yi","year":"2016","unstructured":"Yi, L., Kim, V.G., Ceylan, D., Shen, I.-C., Yan, M., Su, H., Lu, C., Huang, Q., Sheffer, A., Guibas, L.: A scalable active framework for region annotation in 3D shape collections. ACM Trans. Graph. (ToG) 35(6), 1\u201312 (2016)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"3555_CR30","doi-asserted-by":"crossref","unstructured":"Te, G., Hu, W., Zheng, A., Guo, Z.: RGCNN: regularized graph CNN for point cloud segmentation. In: Proceedings of the 26th ACM international conference on multimedia, pp. 746\u2013754 (2018)","DOI":"10.1145\/3240508.3240621"},{"key":"3555_CR31","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":"3555_CR32","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Xiang, S., Pan, C.: Relation-shape convolutional neural network for point cloud analysis. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 8895\u20138904 (2019)","DOI":"10.1109\/CVPR.2019.00910"},{"key":"3555_CR33","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"},{"key":"3555_CR34","doi-asserted-by":"crossref","unstructured":"Huang, Z., Zhao, Z., Li, B., Han, J.: LCPFormer: towards effective 3D point cloud analysis via local context propagation in transformers. IEEE Trans. Circuits Syst. Video Technol. (2023)","DOI":"10.1109\/TCSVT.2023.3247506"},{"issue":"1","key":"3555_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-021-3431-9","volume":"66","author":"L Lin","year":"2023","unstructured":"Lin, L., Huang, P., Fu, C.-W., Xu, K., Zhang, H., Huang, H.: On learning the right attention point for feature enhancement. Sci. China Inf. Sci. 66(1), 1\u201313 (2023)","journal-title":"Sci. China Inf. Sci."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03555-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03555-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03555-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T11:32:57Z","timestamp":1741001577000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03555-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,16]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["3555"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03555-4","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,16]]},"assertion":[{"value":"14 June 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This paper has no studies involving human or animal subjects.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All authors declare that they have the consent to participate.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"consent to participate"}},{"value":"All authors declare that they have consent for publication.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"consent for publication"}}]}}