{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T20:59:00Z","timestamp":1764277140769,"version":"3.37.3"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T00:00:00Z","timestamp":1648598400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T00:00:00Z","timestamp":1648598400000},"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":["Mach Learn"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s10994-022-06148-1","type":"journal-article","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T21:04:27Z","timestamp":1648674267000},"page":"1327-1348","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["SDANet: spatial deep attention-based for point cloud classification and segmentation"],"prefix":"10.1007","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0047-3006","authenticated-orcid":false,"given":"Jiangjiang","family":"Gao","sequence":"first","affiliation":[]},{"given":"Jinhui","family":"Lan","sequence":"additional","affiliation":[]},{"given":"Bingxu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Feifan","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,30]]},"reference":[{"key":"6148_CR1","doi-asserted-by":"publisher","first-page":"5432","DOI":"10.1109\/LRA.2020.3007440","volume":"5","author":"I Alonso","year":"2020","unstructured":"Alonso, I., Riazuelo, L., Montesano, L., Murillo, A. C., & Letters, A. (2020). 3D-MiniNet: Learning a 2D representation from point clouds for fast and efficient 3D LIDAR semantic segmentation. IEEE Robotics and Automation Letters, 5, 5432\u20135439.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"6148_CR2","doi-asserted-by":"crossref","unstructured":"Armeni, I, Sener, O., Zamir, A. R., Jiang, H., Brilakis, I., Fischer, M., & Savarese, S. (2016). 3D Semantic parsing of large-scale indoor spaces. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1534\u20131543.","DOI":"10.1109\/CVPR.2016.170"},{"key":"6148_CR3","doi-asserted-by":"crossref","unstructured":"Boulch, A., Guerry, Y., Saux, B. L., & Audebert, N. J. C., (2017) Graphics.: SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks. Computers & Graphics 71: 189\u2013198.","DOI":"10.1016\/j.cag.2017.11.010"},{"key":"6148_CR4","unstructured":"Chen, L. Z., Li, X. Y., Fan, D. P., Cheng, M. M., Wang, K., & Lu, S. P. (2019). LSANet: Feature learning on point sets by local spatial attention. arXiv preprint arXiv: 1905.05442."},{"key":"6148_CR5","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.neucom.2021.01.095","volume":"438","author":"C Chen","year":"2021","unstructured":"Chen, C., Fragonara, L. Z., & Tsourdos, A. (2021). GAPointNet: Graph attention based point neural network for exploiting local feature of point cloud. Neurocomputing, 438, 122\u2013132.","journal-title":"Neurocomputing"},{"key":"6148_CR6","first-page":"99","volume":"70","author":"Y Cui","year":"2020","unstructured":"Cui, Y., An, Y., Sun, W., Hu, H., & Song, X. (2020). Lightweight attention module for deep learning on classification and segmentation of 3-D point clouds. IEEE Transactions on Instrumentation and Measurement, 70, 99.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"6148_CR7","doi-asserted-by":"crossref","unstructured":"Feng, M., Zhang, L., Lin, X., Gilani, S. Z., & Mian, A. (2019). Point attention network for semantic segmentation of 3D point clouds. arXiv preprint arXiv: 1909.12663.","DOI":"10.1016\/j.patcog.2020.107446"},{"key":"6148_CR8","doi-asserted-by":"crossref","unstructured":"Feng, Y., Zhang, Z., Zhao, X., Ji, R., & Yue, G. (2018). GVCNN: Group-view convolutional neural networks for 3D shape recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 264\u2013272.","DOI":"10.1109\/CVPR.2018.00035"},{"key":"6148_CR9","unstructured":"Gusi, T., Wei, H., Zongming, G., & Amin Z. (2018). Local spectral graph convolution for point set feature learning. arXiv preprint arXiv: 1803.05827."},{"key":"6148_CR10","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., & Markham, A. (2020b). RandLA-Net: Efficient semantic segmentation of large-scale point clouds. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13\u201319.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"6148_CR11","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.cag.2020.06.001","volume":"90","author":"Z Hu","year":"2020","unstructured":"Hu, Z., Zhang, D., Li, S., & Qin, H. J. C. (2020). Graphics.: Attention-based relation and context modeling for point cloud semantic segmentation. Computers & Graphics, 90, 126\u2013134.","journal-title":"Computers & Graphics"},{"key":"6148_CR12","doi-asserted-by":"crossref","unstructured":"Hui, L., Cheng, M., Xie, J., & Yang, J. (2021). Efficient 3D point cloud feature learning for large-scale place recognition. arXiv preprint arXiv: 2101.02374.","DOI":"10.1109\/ICCV48922.2021.00604"},{"key":"6148_CR13","doi-asserted-by":"crossref","unstructured":"Jiang, M., Wu, Y., Zhao, T., Zhao, Z., & Lu, C. (2018). PointSIFT: A SIFT-like network module for 3D point cloud semantic segmentation. arXiv preprint arXiv: 1807.00652.","DOI":"10.1109\/IGARSS.2019.8900102"},{"key":"6148_CR14","unstructured":"Kaul, C., Pears, N., & Manandhar, S. (2019). SAWNet: A spatially aware deep neural network for 3D point cloud processing. arXiv preprint arXiv: 1905.07650."},{"key":"6148_CR15","unstructured":"Kip, F. T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv: 1609.02907 ."},{"key":"6148_CR16","doi-asserted-by":"crossref","unstructured":"Le, T., & Ye, D. (2018). PointGrid: A deep network for 3D shape understanding. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18\u201323.","DOI":"10.1109\/CVPR.2018.00959"},{"key":"6148_CR17","unstructured":"Li, H., Xiong, P., An, J., & Wang, L. (2018). Pyramid attention network for semantic segmentation. arXiv preprint arXiv: 1805.10180."},{"key":"6148_CR18","unstructured":"Li, Y., & Pirk, S. (2016). FPNN: Field probing neural networks for 3D data. arXiv preprint arXiv: 1605.06240."},{"key":"6148_CR19","unstructured":"Liu, S. (2018). Attentional shapecontextnet for point cloud recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4606\u20134615."},{"key":"6148_CR20","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.cag.2019.11.005","volume":"86","author":"Q Lu","year":"2020","unstructured":"Lu, Q., Chen, C., Xie, W., & Luo, Y. J. C. (2020). Graphics.: PointNGCNN: Deep convolutional networks on 3D point clouds with neighborhood graph filters. Computers & Graphics, 86, 42\u201351.","journal-title":"Computers & Graphics"},{"key":"6148_CR21","doi-asserted-by":"crossref","unstructured":"Maturana, D., & Scherer, S. (2015). VoxNet: A 3D convolutional neural network for real-time object recognition. In IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 922\u2013928.","DOI":"10.1109\/IROS.2015.7353481"},{"key":"6148_CR22","doi-asserted-by":"publisher","unstructured":"MengHao, G., JunXiong, C., Zheng-Ning, L., Tai-Jiang, M., Martin, R. R., Hu, S. M. (2020): Pct: Point cloud transformer. arXiv preprint arXiv: 2012.09688. Doi: https:\/\/doi.org\/10.1007\/s41095-021-0229-5.","DOI":"10.1007\/s41095-021-0229-5"},{"key":"6148_CR23","doi-asserted-by":"crossref","unstructured":"Paigwar, A., Erkent, O., Wolf, C., & Laugier, C. (2019). Attentional PointNet for 3D-object detection in point clouds. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1297\u20131306.","DOI":"10.1109\/CVPRW.2019.00169"},{"key":"6148_CR24","unstructured":"Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2017a). PointNet: Deep learning on point sets for 3D classification and segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 652\u2013660."},{"key":"6148_CR25","unstructured":"Qi, C. R., Li, Y., Hao, S., & Guibas, L. J. (2017b). PointNet++: Deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems, pp. 5099\u20135108."},{"key":"6148_CR26","unstructured":"Qiang, C., Ze, L., Keren, F., Zhao, Q., Du, H. (2021). RGB-D salient object detection via 3D convolutional neural networks. arXiv preprint arXiv: 2101.10241v1."},{"key":"6148_CR27","doi-asserted-by":"crossref","unstructured":"Riegler, G., Ulusoy, A. O., & Geiger, A. (2017). OctNet: learning deep 3D representations at high resolutions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3577\u20133586.","DOI":"10.1109\/CVPR.2017.701"},{"key":"6148_CR28","doi-asserted-by":"crossref","unstructured":"Saleh, K., Zeineldin, R. A., Hossny, M., Nahavandi, S., & El-Fishawy, N. (2018b). End-to-end indoor navigation assistance for the visually impaired using monocular camera. In IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3504\u20133510.","DOI":"10.1109\/SMC.2018.00593"},{"key":"6148_CR29","doi-asserted-by":"crossref","unstructured":"Saleh, K., Attia, M., Hossny, M., Hanoun, S., & Nahavandi, S. (2018a). Local motion planning for ground mobile robots via deep imitation learning. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4077\u20134082.","DOI":"10.1109\/SMC.2018.00691"},{"key":"6148_CR30","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli, F., Gori, M., Tsoi, A., Hagenbuchner, M., & Monfardini, G. J. (2009). The graph neural network model. IEEE Transactionson Neural Networks, 20, 61\u201380.","journal-title":"IEEE Transactionson Neural Networks"},{"key":"6148_CR31","doi-asserted-by":"crossref","unstructured":"Shen, Y., Feng, C., Yang, Y., Tian, D. (2018). Mining point cloud local structures by kernel correlation and graph pooling. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4548\u20134557.","DOI":"10.1109\/CVPR.2018.00478"},{"key":"6148_CR32","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., & Learned-Miller, E. J. I. (2015). Multi-view convolutional neural networks for 3D shape recognition. In IEEE International Conference on Computer Vision (ICCV), pp. 945\u2013953.","DOI":"10.1109\/ICCV.2015.114"},{"key":"6148_CR33","unstructured":"Tan, Y., Meng, J., & Yuan, J. (2018). Multi-view harmonized bilinear network for 3D object recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 18\u201323."},{"key":"6148_CR34","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A. (2018). Graph attention networks. arXiv preprint arXiv: 1710.10903v3."},{"key":"6148_CR35","unstructured":"Wang, C., Samari, B., & Siddiqi, K. (2018). Regularized graph CNN for point cloud segmentation. arXiv preprint arXiv: 1806.02952."},{"key":"6148_CR36","doi-asserted-by":"crossref","unstructured":"Wang, L., Huang, Y., Hou, Y., Zhang, S., & Shan, J. (2019b). Graph attention convolution for point cloud semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10288\u201310297.","DOI":"10.1109\/CVPR.2019.01054"},{"key":"6148_CR37","first-page":"1","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M., & Solomon, J. (2019). Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics, 38, 1\u201312.","journal-title":"ACM Transactions on Graphics"},{"key":"6148_CR38","doi-asserted-by":"crossref","unstructured":"Wu, B., Zhou, X., Zhao, S., Yue, X., & Keutzer, K. (2018). SqueezeSegV2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a LiDAR point cloud. arXiv preprint arXiv: 1809.08495.","DOI":"10.1109\/ICRA.2019.8793495"},{"key":"6148_CR39","unstructured":"Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., & Xiao, J. (2015). 3D ShapeNets: A deep representation for volumetric shapes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1912\u20131920."},{"key":"6148_CR40","unstructured":"Xiaobin, H., Yanyang, Y., Wenqi, R., Hongwei, L., Yu, Z. (2020). Feedback graph attention convolutional network for medical image enhancement. In: Image and Video Processing (eess.IV). arXiv preprint arXiv: 2006.13863."},{"key":"6148_CR41","doi-asserted-by":"crossref","unstructured":"Yang, B., Wang, S., Markham, A., & Trigoni, N. (2019). Robust attentional aggregation of deep feature sets for multi-view 3D reconstruction. arXiv preprint arXiv: 1808.00758.","DOI":"10.1007\/s11263-019-01217-w"},{"key":"6148_CR42","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. (2016). A scalable active framework for region annotation in 3d shape collections. ACM Transactions on Graphics, 35, 1\u201312.","journal-title":"ACM Transactions on Graphics"},{"key":"6148_CR43","doi-asserted-by":"crossref","unstructured":"Zermas, D., Izzat, I., & Papanikolopoulos, N. (2017). Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications. In IEEE International Conference on Robotics and Automation (ICRA), pp. 5067\u20135073.","DOI":"10.1109\/ICRA.2017.7989591"},{"key":"6148_CR44","unstructured":"Zhang, K., Hao, M., Wang, J., Silva, C. D., & Fu, C. (2019). Linked dynamic graph CNN: Learning on point cloud via linking hierarchical features. arXiv preprint arXiv: 1904.10014."},{"key":"6148_CR45","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.neucom.2020.06.095","volume":"413","author":"W Zhang","year":"2020","unstructured":"Zhang, W., Su, S., Wang, B., & Sun, L. J. N. (2020). Local k-NNs pattern in omni-direction graph convolution neural network for 3D point clouds. Neurocomputing, 413, 487\u2013498.","journal-title":"Neurocomputing"},{"key":"6148_CR46","unstructured":"Zhiheng, K., & Ning, L. (2019). PyramNet: Point cloud pyramid attention network and graph embedding module for classification and segmentation. arXiv preprint arXiv: 1906.03299."},{"key":"6148_CR47","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Mottaghi, R., Kolve, E., Lim, J. J., & Farhadi, A. (2017). Target-driven visual navigation in indoor scenes using deep reinforcement learning. In IEEE International Conference on Robotics and Automation (ICRA), pp. 3357\u20133364.","DOI":"10.1109\/ICRA.2017.7989381"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06148-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-022-06148-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06148-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T00:07:33Z","timestamp":1680134853000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-022-06148-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,30]]},"references-count":47,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["6148"],"URL":"https:\/\/doi.org\/10.1007\/s10994-022-06148-1","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"type":"print","value":"0885-6125"},{"type":"electronic","value":"1573-0565"}],"subject":[],"published":{"date-parts":[[2022,3,30]]},"assertion":[{"value":"25 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}