{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:59:07Z","timestamp":1742921947961,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819785070"},{"type":"electronic","value":"9789819785087"}],"license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"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\/978-981-97-8508-7_29","type":"book-chapter","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T06:06:07Z","timestamp":1730527567000},"page":"417-430","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TriEn-Net: Non-parametric Representation Learning for Large-Scale Point Cloud Semantic Segmentation"],"prefix":"10.1007","author":[{"given":"Yifei","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jixiang","family":"Miao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9634-3125","authenticated-orcid":false,"given":"Anan","family":"Du","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Gu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5668-833X","authenticated-orcid":false,"given":"Shuchao","family":"Pang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"key":"29_CR1","unstructured":"Armeni, I., Sax, S., Zamir, A.R., Savarese, S.: Joint 2d-3d-semantic data for indoor scene understanding. arXiv preprint arXiv:1702.01105 (2017)"},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Bai, J., Yang, Z., Guan, Y., Yu, Q.: Pcrt: Multi-branch point cloud reconstruction from a single image with transformers. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp. 259\u2013270. Springer (2023)","DOI":"10.1007\/978-981-99-8432-9_21"},{"key":"29_CR3","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":"29_CR4","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3d object detection network for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1907\u20131915 (2017)","DOI":"10.1109\/CVPR.2017.691"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y., Shi, P.: Rotation-invariant completion network. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp. 115\u2013127. Springer (2023)","DOI":"10.1007\/978-981-99-8432-9_10"},{"issue":"20","key":"29_CR6","doi-asserted-by":"publisher","first-page":"7868","DOI":"10.3390\/s22207868","volume":"22","author":"A Diab","year":"2022","unstructured":"Diab, A., Kashef, R., Shaker, A.: Deep learning for lidar point cloud classification in remote sensing. Sensors 22(20), 7868 (2022)","journal-title":"Sensors"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Du, A., Zhou, T., Pang, S., Wu, Q., Zhang, J.: Pcl: Point contrast and labeling for weakly supervised point cloud semantic segmentation. IEEE Transactions on Multimedia pp. 1\u201312 (2024)","DOI":"10.1109\/TMM.2024.3383674"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Fan, S., Dong, Q., Zhu, F., Lv, Y., Ye, P., Wang, F.Y.: Scf-net: Learning spatial contextual features for large-scale point cloud segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14504\u201314513 (2021)","DOI":"10.1109\/CVPR46437.2021.01427"},{"key":"29_CR9","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":"29_CR10","doi-asserted-by":"crossref","unstructured":"Landrieu, L., Simonovsky, M.: Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4558\u20134567 (2018)","DOI":"10.1109\/CVPR.2018.00479"},{"key":"29_CR11","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018)"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Lu, L., Chen, Z., Lu, X., Rao, Y., Li, L., Pang, S.: Uniads: Universal architecture-distiller search for distillation gap. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a038, pp. 14167\u201314174 (2024)","DOI":"10.1609\/aaai.v38i13.29327"},{"key":"29_CR13","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"},{"issue":"11","key":"29_CR14","doi-asserted-by":"publisher","first-page":"6776","DOI":"10.1109\/TCYB.2022.3195447","volume":"53","author":"S Pang","year":"2022","unstructured":"Pang, S., Du, A., Orgun, M.A., Wang, Y., Sheng, Q.Z., Wang, S., Huang, X., Yu, Z.: Beyond cnns: exploiting further inherent symmetries in medical image segmentation. IEEE Transactions on Cybernetics 53(11), 6776\u20136787 (2022)","journal-title":"IEEE Transactions on Cybernetics"},{"key":"29_CR15","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.neunet.2021.03.006","volume":"140","author":"S Pang","year":"2021","unstructured":"Pang, S., Du, A., Orgun, M.A., Wang, Y., Yu, Z.: Tumor attention networks: Better feature selection, better tumor segmentation. Neural Netw. 140, 203\u2013222 (2021)","journal-title":"Neural Netw."},{"key":"29_CR16","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1016\/j.neunet.2018.09.001","volume":"108","author":"AV Phan","year":"2018","unstructured":"Phan, A.V., Le Nguyen, M., Nguyen, Y.L.H., Bui, L.T.: Dgcnn: A convolutional neural network over large-scale labeled graphs. Neural Netw. 108, 533\u2013543 (2018)","journal-title":"Neural Netw."},{"key":"29_CR17","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":"29_CR18","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":"29_CR19","unstructured":"Rahaman, N., Baratin, A., Arpit, D., Draxler, F., Lin, M., Hamprecht, F., Bengio, Y., Courville, A.: On the spectral bias of neural networks. In: International Conference on Machine Learning, pp. 5301\u20135310. PMLR (2019)"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Robert, D., Raguet, H., Landrieu, L.: Efficient 3d semantic segmentation with superpoint transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 17195\u201317204 (2023)","DOI":"10.1109\/ICCV51070.2023.01577"},{"key":"29_CR21","doi-asserted-by":"crossref","unstructured":"Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M.H., Kautz, J.: 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":"29_CR22","first-page":"7537","volume":"33","author":"M Tancik","year":"2020","unstructured":"Tancik, M., Srinivasan, P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J., Ng, R.: Fourier features let networks learn high frequency functions in low dimensional domains. Adv. Neural. Inf. Process. Syst. 33, 7537\u20137547 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Tatarchenko, M., Park, J., Koltun, V., Zhou, Q.Y.: Tangent convolutions for dense prediction in 3d. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3887\u20133896 (2018)","DOI":"10.1109\/CVPR.2018.00409"},{"key":"29_CR24","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 (ICCV), pp. 6411\u20136420 (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"key":"29_CR25","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Advances in Neural Information Processing Systems 30 (2017)"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Wu, B., Wan, A., Yue, X., Keutzer, K.: Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1887\u20131893. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8462926"},{"key":"29_CR27","doi-asserted-by":"crossref","unstructured":"Wu, B., Zhou, X., Zhao, S., Yue, X., Keutzer, K.: Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 4376\u20134382. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8793495"},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Wu, H., Yan, L., Xie, H., Wei, P., Dai, J., Gao, Z., Zhang, R.: A voxel-based multiview point cloud refinement method via factor graph optimization. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp. 234\u2013245. Springer (2023)","DOI":"10.1007\/978-981-99-8432-9_19"},{"key":"29_CR29","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"},{"issue":"1","key":"29_CR30","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s11263-019-01217-w","volume":"128","author":"B Yang","year":"2020","unstructured":"Yang, B., Wang, S., Markham, A., Trigoni, N.: Robust attentional aggregation of deep feature sets for multi-view 3d reconstruction. Int. J. Comput. Vision 128(1), 53\u201373 (2020)","journal-title":"Int. J. Comput. Vision"},{"key":"29_CR31","unstructured":"Yang, L., Zhang, R.Y., Li, L., Xie, X.: Simam: A simple, parameter-free attention module for convolutional neural networks. In: International Conference on Machine Learning, pp. 11863\u201311874. PMLR (2021)"},{"issue":"1","key":"29_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/19479832.2016.1160960","volume":"8","author":"J Zhang","year":"2017","unstructured":"Zhang, J., Lin, X.: Advances in fusion of optical imagery and lidar point cloud applied to photogrammetry and remote sensing. Int. J. Image Data Fusion 8(1), 1\u201331 (2017)","journal-title":"Int. J. Image Data Fusion"},{"key":"29_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, R., Wang, L., Guo, Z., Wang, Y., Gao, P., Li, H., Shi, J.: Parameter is not all you need: Starting from non-parametric networks for 3d point cloud analysis. arXiv preprint arXiv:2303.08134 (2023)","DOI":"10.1109\/CVPR52729.2023.00517"},{"key":"29_CR34","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Fu, C.W., Jia, J.: Pointweb: Enhancing local neighborhood features for point cloud processing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5565\u20135573 (2019)","DOI":"10.1109\/CVPR.2019.00571"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8508-7_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T06:17:40Z","timestamp":1730528260000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8508-7_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"ISBN":["9789819785070","9789819785087"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8508-7_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,3]]},"assertion":[{"value":"3 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","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":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}