{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T14:15:02Z","timestamp":1788185702276,"version":"build-2803163510"},"reference-count":59,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"publisher","award":["2025JC-JCQN-091"],"award-info":[{"award-number":["2025JC-JCQN-091"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"publisher","award":["2024QY-SZX-23"],"award-info":[{"award-number":["2024QY-SZX-23"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62125305"],"award-info":[{"award-number":["62125305"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.114465","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:54:56Z","timestamp":1784303696000},"page":"114465","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PE","title":["Curve3D: Curvature-aware masked autoencoder for self-supervised point cloud understanding"],"prefix":"10.1016","volume":"180","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6420-0781","authenticated-orcid":false,"given":"Yiding","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoyi","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haozhe","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huimin","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihua","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.114465_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112800","article-title":"HyperPoint: Multimodal 3D foundation model in hyperbolic space","volume":"173","author":"Sun","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114465_b2","doi-asserted-by":"crossref","DOI":"10.1109\/TMM.2026.3705202","article-title":"Align then adapt: Rethinking parameter-efficient transfer learning in 4D perception","author":"Sun","year":"2026","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.patcog.2026.114465_b3","doi-asserted-by":"crossref","unstructured":"P. Li, Y. Sun, H. Cheng, PointDico: Contrastive 3D Representation Learning Guided by Diffusion Models, in: Proc Int Jt Conf Neural Netw, 2025, pp. 1\u20139.","DOI":"10.1109\/IJCNN64981.2025.11227674"},{"key":"10.1016\/j.patcog.2026.114465_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109800","article-title":"MKConv: Multidimensional feature representation for point cloud analysis","volume":"143","author":"Woo","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114465_b5","unstructured":"Z. Qi, R. Dong, G. Fan, Z. Ge, X. Zhang, K. Ma, L. Yi, Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining, in: Int. Conf. Mach. Learn., 2023."},{"key":"10.1016\/j.patcog.2026.114465_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110086","article-title":"Joint representation learning for text and 3D point cloud","volume":"147","author":"Huang","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114465_b7","doi-asserted-by":"crossref","unstructured":"X. Han, Y. Sun, C. Lu, Rethinking Regressor in 3D Gaussian Pretraining, in: Pattern Recognit. Comput. Vis., Singapore, 2026, pp. 177\u2013190.","DOI":"10.1007\/978-981-95-5737-0_13"},{"key":"10.1016\/j.patcog.2026.114465_b8","doi-asserted-by":"crossref","unstructured":"K. He, X. Chen, S. Xie, Y. Li, P. Doll\u00e1r, R. Girshick, Masked autoencoders are scalable vision learners, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2022, pp. 16000\u201316009.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"10.1016\/j.patcog.2026.114465_b9","series-title":"Eur. Conf. Comput. Vis.","first-page":"604","article-title":"Masked autoencoders for point cloud self-supervised learning","author":"Pang","year":"2022"},{"key":"10.1016\/j.patcog.2026.114465_b10","first-page":"32653","article-title":"Pointmamba: A simple state space model for point cloud analysis","volume":"37","author":"Liang","year":"2024","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114465_b11","first-page":"80303","article-title":"Pcp-mae: Learning to predict centers for point masked autoencoders","volume":"37","author":"Zhang","year":"2024","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114465_b12","unstructured":"C.R. Qi, H. Su, K. Mo, L.J. Guibas, Pointnet: Deep learning on point sets for 3d classification and segmentation, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2017, pp. 652\u2013660."},{"key":"10.1016\/j.patcog.2026.114465_b13","unstructured":"C.R. Qi, L. Yi, H. Su, L.J. Guibas, Pointnet++: Deep hierarchical feature learning on point sets in a metric space, in: Proc. Adv. Neural Inf. Process. Syst., 2017."},{"key":"10.1016\/j.patcog.2026.114465_b14","series-title":"PointCoT: A multi-modal benchmark for explicit 3D geometric reasoning","author":"Zhang","year":"2026"},{"key":"10.1016\/j.patcog.2026.114465_b15","unstructured":"T. Brown, B. Mann, N. Ryder, M. Subbiah, J.D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., Language models are few-shot learners, in: Proc. Adv. Neural Inf. Process. Syst., 2020."},{"key":"10.1016\/j.patcog.2026.114465_b16","doi-asserted-by":"crossref","first-page":"8360","DOI":"10.1109\/TMM.2023.3282568","article-title":"Point-LGMask: Local and global contexts embedding for point cloud pre-training with multi-ratio masking","volume":"26","author":"Tang","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.patcog.2026.114465_b17","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1109\/TIP.2024.3520008","article-title":"Rethinking masked representation learning for 3D point cloud understanding","volume":"34","author":"Wang","year":"2025","journal-title":"IEEE Trans. Image Process."},{"issue":"7","key":"10.1016\/j.patcog.2026.114465_b18","doi-asserted-by":"crossref","first-page":"12964","DOI":"10.1109\/TNNLS.2024.3479309","article-title":"Point-MPP: Point cloud self-supervised learning from masked position prediction","volume":"36","author":"Fan","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patcog.2026.114465_b19","doi-asserted-by":"crossref","unstructured":"S. Xie, J. Gu, D. Guo, C.R. Qi, L. Guibas, O. Litany, Pointcontrast: Unsupervised pre-training for 3d point cloud understanding, in: Eur. Conf. Comput. Vis., 2020, pp. 574\u2013591.","DOI":"10.1007\/978-3-030-58580-8_34"},{"key":"10.1016\/j.patcog.2026.114465_b20","doi-asserted-by":"crossref","unstructured":"M. Afham, I. Dissanayake, D. Dissanayake, A. Dharmasiri, K. Thilakarathna, R. Rodrigo, Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2022, pp. 9902\u20139912.","DOI":"10.1109\/CVPR52688.2022.00967"},{"key":"10.1016\/j.patcog.2026.114465_b21","doi-asserted-by":"crossref","unstructured":"R. Zhang, Z. Guo, P. Gao, R. Fang, B. Zhao, D. Wang, Y. Qiao, H. Li, Point-m2ae: Multi-scale masked autoencoders for hierarchical point cloud pre-training, in: Proc. Adv. Neural Inf. Process. Syst., 2022.","DOI":"10.52202\/068431-1962"},{"key":"10.1016\/j.patcog.2026.114465_b22","unstructured":"R. Dong, Z. Qi, L. Zhang, J. Zhang, J. Sun, Z. Ge, L. Yi, K. Ma, Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?, in: Proc. Int. Conf. Learn. Representations, 2023."},{"key":"10.1016\/j.patcog.2026.114465_b23","doi-asserted-by":"crossref","unstructured":"Z. Guo, R. Zhang, L. Qiu, X. Li, P.-A. Heng, Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training, in: Proc. IJCAI, 2023, pp. 791\u2013799.","DOI":"10.24963\/ijcai.2023\/88"},{"key":"10.1016\/j.patcog.2026.114465_b24","doi-asserted-by":"crossref","unstructured":"R. Zhang, L. Wang, Y. Qiao, P. Gao, H. Li, Learning 3D Representations from 2D Pre-Trained Models via Image-to-Point Masked Autoencoders, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2023, pp. 21769\u201321780.","DOI":"10.1109\/CVPR52729.2023.02085"},{"key":"10.1016\/j.patcog.2026.114465_b25","unstructured":"A. Gu, T. Dao, Mamba: Linear-time sequence modeling with selective state spaces, in: First Conference on Language Modeling, 2024."},{"key":"10.1016\/j.patcog.2026.114465_b26","unstructured":"L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, X. Wang, Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model, in: Proc. Int. Conf. Mach. Learn., 2024."},{"key":"10.1016\/j.patcog.2026.114465_b27","article-title":"Point cloud Mamba: Point cloud learning via state space model","author":"Zhang","year":"2025","journal-title":"AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.patcog.2026.114465_b28","doi-asserted-by":"crossref","unstructured":"W. Yu, X. Wang, Mambaout: Do we really need mamba for vision?, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2025, pp. 4484\u20134496.","DOI":"10.1109\/CVPR52734.2025.00423"},{"key":"10.1016\/j.patcog.2026.114465_b29","doi-asserted-by":"crossref","unstructured":"X. Yu, L. Tang, Y. Rao, T. Huang, J. Zhou, J. Lu, Point-bert: Pre-training 3d point cloud transformers with masked point modeling, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2022, pp. 19313\u201319322.","DOI":"10.1109\/CVPR52688.2022.01871"},{"issue":"12","key":"10.1016\/j.patcog.2026.114465_b30","doi-asserted-by":"crossref","first-page":"12158","DOI":"10.1109\/TCSVT.2024.3430904","article-title":"PTM: Torus masking for 3D representation learning guided by robust and trusted teachers","volume":"34","author":"Cheng","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.patcog.2026.114465_b31","unstructured":"A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, \u0141. Kaiser, I. Polosukhin, Attention is all you need, in: Proc. Adv. Neural Inf. Process. Syst., 2017."},{"key":"10.1016\/j.patcog.2026.114465_b32","doi-asserted-by":"crossref","unstructured":"H. Wang, Q. Liu, X. Yue, J. Lasenby, M.J. Kusner, Unsupervised point cloud pre-training via occlusion completion, in: Proc. IEEE\/CVF Int. Conf. Comput. Vis., 2021, pp. 9782\u20139792.","DOI":"10.1109\/ICCV48922.2021.00964"},{"key":"10.1016\/j.patcog.2026.114465_b33","doi-asserted-by":"crossref","unstructured":"Y. Zha, J. Wang, T. Dai, B. Chen, Z. Wang, S.-T. Xia, Instance-aware dynamic prompt tuning for pre-trained point cloud models, in: Proc. IEEE\/CVF Int. Conf. Comput. Vis., 2023, pp. 14161\u201314170.","DOI":"10.1109\/ICCV51070.2023.01302"},{"key":"10.1016\/j.patcog.2026.114465_b34","doi-asserted-by":"crossref","unstructured":"Y. Zha, N. Li, Y. Wang, T. Dai, H. Guo, B. Chen, Z. Wang, Z. Ouyang, S.-T. Xia, LCM: Locally Constrained Compact Point Cloud Model for Masked Point Modeling, in: Proc. Adv. Neural Inf. Process. Syst., vol. 37, 2024, pp. 104816\u2013104842.","DOI":"10.52202\/079017-3329"},{"key":"10.1016\/j.patcog.2026.114465_b35","doi-asserted-by":"crossref","unstructured":"H. Fan, H. Su, L.J. Guibas, A point set generation network for 3d object reconstruction from a single image, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2017, pp. 605\u2013613.","DOI":"10.1109\/CVPR.2017.264"},{"key":"10.1016\/j.patcog.2026.114465_b36","series-title":"Shapenet: An information-rich 3d model repository","author":"Chang","year":"2015"},{"key":"10.1016\/j.patcog.2026.114465_b37","doi-asserted-by":"crossref","DOI":"10.1145\/3326362","article-title":"Dynamic graph cnn for learning on point clouds","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"10.1016\/j.patcog.2026.114465_b38","unstructured":"Y. Li, R. Bu, M. Sun, W. Wu, X. Di, B. Chen, PointCNN: Convolution On X-Transformed Points, in: Proc. Adv. Neural Inf. Process. Syst., vol. 31, 2018."},{"key":"10.1016\/j.patcog.2026.114465_b39","unstructured":"A. Goyal, H. Law, B. Liu, A. Newell, J. Deng, Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline, in: Proc. Int. Conf. Mach. Learn., 2021."},{"key":"10.1016\/j.patcog.2026.114465_b40","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s41095-021-0229-5","article-title":"PCT: Point cloud transformer","volume":"7","author":"Guo","year":"2020","journal-title":"Comput. Vis. Media"},{"key":"10.1016\/j.patcog.2026.114465_b41","unstructured":"X. Ma, C. Qin, H. You, H. Ran, Y. Fu, Rethinking network design and local geometry in point cloud: A simple residual MLP framework, in: Proc. Int. Conf. Learn. Represent., 2022."},{"key":"10.1016\/j.patcog.2026.114465_b42","doi-asserted-by":"crossref","unstructured":"G. Qian, Y. Li, H. Peng, J. Mai, H. Hammoud, M. Elhoseiny, B. Ghanem, Pointnext: Revisiting pointnet++ with improved training and scaling strategies, in: Proc. Adv. Neural Inf. Process. Syst., 2022, pp. 23192\u201323204.","DOI":"10.52202\/068431-1685"},{"key":"10.1016\/j.patcog.2026.114465_b43","doi-asserted-by":"crossref","unstructured":"Z. Wang, X. Yu, Y. Rao, J. Zhou, J. Lu, P2p: Tuning pre-trained image models for point cloud analysis with point-to-pixel prompting, in: Proc. Adv. Neural Inf. Process. Syst., 2022, pp. 14388\u201314402.","DOI":"10.52202\/068431-1046"},{"key":"10.1016\/j.patcog.2026.114465_b44","series-title":"Eur. Conf. Comput. Vis.","first-page":"657","article-title":"Masked discrimination for self-supervised learning on point clouds","author":"Liu","year":"2022"},{"key":"10.1016\/j.patcog.2026.114465_b45","article-title":"Pointgpt: Auto-regressively generative pre-training from point clouds","volume":"36","author":"Chen","year":"2024","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114465_b46","doi-asserted-by":"crossref","unstructured":"Y. Zha, H. Ji, J. Li, R. Li, T. Dai, B. Chen, Z. Wang, S.-T. Xia, Towards compact 3d representations via point feature enhancement masked autoencoders, in: Proc. AAAI Conf. Artif. Intell., vol. 38, 2024, pp. 6962\u20136970, no. 7.","DOI":"10.1609\/aaai.v38i7.28522"},{"key":"10.1016\/j.patcog.2026.114465_b47","doi-asserted-by":"crossref","unstructured":"X. Han, Y. Tang, Z. Wang, X. Li, Mamba3d: Enhancing local features for 3d point cloud analysis via state space model, in: Proc. ACM Int. Conf. Multimedia, 2024, pp. 4995\u20135004.","DOI":"10.1145\/3664647.3681173"},{"key":"10.1016\/j.patcog.2026.114465_b48","doi-asserted-by":"crossref","unstructured":"M.A. Uy, Q.-H. Pham, B.-S. Hua, T. Nguyen, S.-K. Yeung, Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data, in: Proc. IEEE\/CVF Int. Conf. Comput. Vis., 2019, pp. 1588\u20131597.","DOI":"10.1109\/ICCV.2019.00167"},{"key":"10.1016\/j.patcog.2026.114465_b49","doi-asserted-by":"crossref","unstructured":"Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, J. Xiao, 3d shapenets: A deep representation for volumetric shapes, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2015, pp. 1912\u20131920.","DOI":"10.1109\/CVPR.2015.7298801"},{"key":"10.1016\/j.patcog.2026.114465_b50","doi-asserted-by":"crossref","unstructured":"Z. Wang, X. Yu, Y. Rao, J. Zhou, J. Lu, Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models, in: Proc. IEEE\/CVF Int. Conf. Comput. Vis., 2023, pp. 5640\u20135650.","DOI":"10.1109\/ICCV51070.2023.00519"},{"issue":"6","key":"10.1016\/j.patcog.2026.114465_b51","doi-asserted-by":"crossref","DOI":"10.1145\/2980179.2980238","article-title":"A scalable active framework for region annotation in 3D shape collections","volume":"35","author":"Yi","year":"2016","journal-title":"ACM Trans. Graph."},{"key":"10.1016\/j.patcog.2026.114465_b52","doi-asserted-by":"crossref","unstructured":"L. Jing, L. Zhang, Y. Tian, Self-Supervised Feature Learning by Cross-Modality and Cross-View Correspondences, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, 2021, pp. 1581\u20131591.","DOI":"10.1109\/CVPRW53098.2021.00174"},{"key":"10.1016\/j.patcog.2026.114465_b53","doi-asserted-by":"crossref","unstructured":"A. Dai, A.X. Chang, M. Savva, M. Halber, T. Funkhouser, M. Nie\u00dfner, Scannet: Richly-annotated 3d reconstructions of indoor scenes, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2017, pp. 5828\u20135839.","DOI":"10.1109\/CVPR.2017.261"},{"key":"10.1016\/j.patcog.2026.114465_b54","doi-asserted-by":"crossref","unstructured":"I. Misra, R. Girdhar, A. Joulin, An end-to-end transformer model for 3d object detection, in: Proc. IEEE Int. Conf. Comput. Vis., 2021, pp. 2906\u20132917.","DOI":"10.1109\/ICCV48922.2021.00290"},{"key":"10.1016\/j.patcog.2026.114465_b55","doi-asserted-by":"crossref","unstructured":"A. Chen, K. Zhang, R. Zhang, Z. Wang, Y. Lu, Y. Guo, S. Zhang, Pimae: Point cloud and image interactive masked autoencoders for 3d object detection, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2023, pp. 5291\u20135301.","DOI":"10.1109\/CVPR52729.2023.00512"},{"key":"10.1016\/j.patcog.2026.114465_b56","unstructured":"C.R. Qi, O. Litany, K. He, L.J. Guibas, Deep hough voting for 3d object detection in point clouds, in: Proc. IEEE Int. Conf. Comput. Vis., 2019, pp. 9277\u20139286."},{"key":"10.1016\/j.patcog.2026.114465_b57","doi-asserted-by":"crossref","unstructured":"S. Huang, Y. Xie, S.-C. Zhu, Y. Zhu, Spatio-temporal self-supervised representation learning for 3d point clouds, in: Proc. IEEE Int. Conf. Comput. Vis., 2021, pp. 6535\u20136545.","DOI":"10.1109\/ICCV48922.2021.00647"},{"key":"10.1016\/j.patcog.2026.114465_b58","doi-asserted-by":"crossref","unstructured":"Z. Zhang, R. Girdhar, A. Joulin, I. Misra, Self-Supervised Pretraining of 3D Features on any Point-Cloud, in: Proc. IEEE\/CVF Int. Conf. Comput. Vis., 2021, pp. 10232\u201310243.","DOI":"10.1109\/ICCV48922.2021.01009"},{"key":"10.1016\/j.patcog.2026.114465_b59","doi-asserted-by":"crossref","unstructured":"I. Armeni, O. Sener, A.R. Zamir, H. Jiang, I. Brilakis, M. Fischer, S. Savarese, 3D Semantic Parsing of Large-Scale Indoor Spaces, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2016, pp. 1534\u20131543.","DOI":"10.1109\/CVPR.2016.170"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326014299?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326014299?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T13:28:12Z","timestamp":1788182892000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326014299"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":59,"alternative-id":["S0031320326014299"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114465","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Curve3D: Curvature-aware masked autoencoder for self-supervised point cloud understanding","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114465","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114465"}}