{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T09:10:18Z","timestamp":1771492218685,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031781124","type":"print"},{"value":"9783031781131","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"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\/978-3-031-78113-1_27","type":"book-chapter","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T17:01:29Z","timestamp":1733245289000},"page":"411-426","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Content-Aware Feature Upsampling for Voxel-Based 3D Semantic Segmentation"],"prefix":"10.1007","author":[{"given":"Yu","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruigang","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingyong","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"27_CR1","doi-asserted-by":"crossref","unstructured":"Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: Semantickitti: A dataset for semantic scene understanding of lidar sequences. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 9297\u20139307 (2019)","DOI":"10.1109\/ICCV.2019.00939"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 11621\u201311631 (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic convolution: Attention over convolution kernels. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 11030\u201311039 (2020)","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, Y., Zhang, X., Sun, J., Jia, J.: Focal sparse convolutional networks for 3d object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 5428\u20135437 (2022)","DOI":"10.1109\/CVPR52688.2022.00535"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Cheng, R., Razani, R., Taghavi, E., Li, E., Liu, B.: 2-s3net: Attentive feature fusion with adaptive feature selection for sparse semantic segmentation network. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 12547\u201312556 (2021)","DOI":"10.1109\/CVPR46437.2021.01236"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Choy, C., Gwak, J., Savarese, S.: 4d spatio-temporal convnets: Minkowski convolutional neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 3075\u20133084 (2019)","DOI":"10.1109\/CVPR.2019.00319"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. In: Proceedings of the IEEE international conference on computer vision. pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Graham, B., Engelcke, M., Van Der\u00a0Maaten, L.: 3d semantic segmentation with submanifold sparse convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 9224\u20139232 (2018)","DOI":"10.1109\/CVPR.2018.00961"},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Graham, B., Van\u00a0der Maaten, L.: Submanifold sparse convolutional networks. arXiv preprint arXiv:1706.01307 (2017)","DOI":"10.1109\/CVPR.2018.00961"},{"key":"27_CR10","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":"27_CR11","doi-asserted-by":"crossref","unstructured":"Kong, L., Liu, Y., Chen, R., Ma, Y., Zhu, X., Li, Y., Hou, Y., Qiao, Y., Liu, Z.: Rethinking range view representation for lidar segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 228\u2013240 (2023)","DOI":"10.1109\/ICCV51070.2023.00028"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Lai, X., Chen, Y., Lu, F., Liu, J., Jia, J.: Spherical transformer for lidar-based 3d recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 17545\u201317555 (2023)","DOI":"10.1109\/CVPR52729.2023.01683"},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"Li, L., Shum, H.P., Breckon, T.P.: Less is more: Reducing task and model complexity for 3d point cloud semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9361\u20139371 (2023)","DOI":"10.1109\/CVPR52729.2023.00903"},{"key":"27_CR14","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":"27_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, R., Li, X., Kong, L., Yang, Y., Xia, Z., Bai, Y., Zhu, X., Ma, Y., Li, Y., et\u00a0al.: Uniseg: A unified multi-modal lidar segmentation network and the openpcseg codebase. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 21662\u201321673 (2023)","DOI":"10.1109\/ICCV51070.2023.01980"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: Rangenet++: Fast and accurate lidar semantic segmentation. In: 2019 IEEE\/RSJ international conference on intelligent robots and systems (IROS). pp. 4213\u20134220. IEEE (2019)","DOI":"10.1109\/IROS40897.2019.8967762"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE international conference on computer vision. pp. 1520\u20131528 (2015)","DOI":"10.1109\/ICCV.2015.178"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Pan, Y., Gao, B., Mei, J., Geng, S., Li, C., Zhao, H.: Semanticposs: A point cloud dataset with large quantity of dynamic instances. In: 2020 IEEE Intelligent Vehicles Symposium (IV). pp. 687\u2013693. IEEE (2020)","DOI":"10.1109\/IV47402.2020.9304596"},{"key":"27_CR19","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":"27_CR20","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017)"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et\u00a0al.: Scalability in perception for autonomous driving: Waymo open dataset. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 2446\u20132454 (2020)","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"27_CR23","first-page":"302","volume":"4","author":"H Tang","year":"2022","unstructured":"Tang, H., Liu, Z., Li, X., Lin, Y., Han, S.: Torchsparse: Efficient point cloud inference engine. Proceedings of Machine Learning and Systems 4, 302\u2013315 (2022)","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Tang, H., Liu, Z., Zhao, S., Lin, Y., Lin, J., Wang, H., Han, S.: Searching efficient 3d architectures with sparse point-voxel convolution. In: European conference on computer vision. pp. 685\u2013702. Springer (2020)","DOI":"10.1007\/978-3-030-58604-1_41"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Tang, H., Yang, S., Liu, Z., Hong, K., Yu, Z., Li, X., Dai, G., Wang, Y., Han, S.: Torchsparse++: Efficient point cloud engine. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 202\u2013209 (2023)","DOI":"10.1109\/CVPRW59228.2023.00025"},{"key":"27_CR26","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":"27_CR27","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, K., Xu, R., Liu, Z., Loy, C.C., Lin, D.: Carafe: Content-aware reassembly of features. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 3007\u20133016 (2019)","DOI":"10.1109\/ICCV.2019.00310"},{"key":"27_CR28","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":"27_CR29","doi-asserted-by":"crossref","unstructured":"Wu, X., Jiang, L., Wang, P.S., Liu, Z., Liu, X., Qiao, Y., Ouyang, W., He, T., Zhao, H.: Point transformer v3: Simpler, faster, stronger. arXiv preprint arXiv:2312.10035 (2023)","DOI":"10.1109\/CVPR52733.2024.00463"},{"key":"27_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-58604-1_1","volume-title":"Computer Vision \u2013 ECCV 2020","author":"C Xu","year":"2020","unstructured":"Xu, C., Wu, B., Wang, Z., Zhan, W., Vajda, P., Keutzer, K., Tomizuka, M.: SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12373, pp. 1\u201319. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58604-1_1"},{"key":"27_CR31","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, R., Dou, J., Zhu, Y., Sun, J., Pu, S.: Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 16024\u201316033 (2021)","DOI":"10.1109\/ICCV48922.2021.01572"},{"key":"27_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, Z., David, P., Yue, X., Xi, Z., Gong, B., Foroosh, H.: Polarnet: An improved grid representation for online lidar point clouds semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 9601\u20139610 (2020)","DOI":"10.1109\/CVPR42600.2020.00962"},{"key":"27_CR33","doi-asserted-by":"crossref","unstructured":"Zhu, X., Zhou, H., Wang, T., Hong, F., Ma, Y., Li, W., Li, H., Lin, D.: Cylindrical and asymmetrical 3d convolution networks for lidar segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 9939\u20139948 (2021)","DOI":"10.1109\/CVPR46437.2021.00981"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78113-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T17:07:18Z","timestamp":1733245638000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78113-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"ISBN":["9783031781124","9783031781131"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78113-1_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"4 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}