{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:32:30Z","timestamp":1790267550191,"version":"4.1.0"},"publisher-location":"Cham","reference-count":69,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032370372","type":"print"},{"value":"9783032370389","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-37038-9_32","type":"book-chapter","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:37:13Z","timestamp":1789123033000},"page":"557-576","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Tri-Efficient Transfer Learning for\u00a0Point Cloud Videos"],"prefix":"10.1007","author":[{"given":"Yiding","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongxu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihua","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haozhe","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengqiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengcheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaowei","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghao","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,12]]},"reference":[{"key":"32_CR1","doi-asserted-by":"crossref","unstructured":"Afham, M., Dissanayake, I., Dissanayake, D., Dharmasiri, A., Thilakarathna, K., Rodrigo, R.: Crosspoint: self-supervised cross-modal contrastive learning for 3D point cloud understanding. In: IEEE Conference Computer Vision Pattern Recogition, pp. 9902\u20139912 (2022)","DOI":"10.1109\/CVPR52688.2022.00967"},{"key":"32_CR2","doi-asserted-by":"crossref","unstructured":"Ai, Z., Liu, Z., Lei, Y., Cui, Z., Zou, X., Zhou, J.: GaPrompt: geometry-aware point cloud prompt for 3D vision model. In: International Conference on Machine Learning, pp. 796\u2013808 (2025) PMLR","DOI":"10.1109\/TPAMI.2026.3729984"},{"issue":"7","key":"32_CR3","doi-asserted-by":"publisher","first-page":"6832","DOI":"10.1109\/TCSVT.2025.3538684","volume":"35","author":"Y Bao","year":"2025","unstructured":"Bao, Y., et al.: 3D Gaussian Splatting: survey, technologies, challenges, and opportunities. IEEE Trans. Circuits Syst. Video Technol. 35(7), 6832\u20136852 (2025)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"32_CR4","unstructured":"Bao, Y., Liao, J., Huo, J., Gao, Y.: Distractor-free generalizable 3D gaussian splatting. In: International Conference on Machine Learning (2026)"},{"key":"32_CR5","unstructured":"Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository (2015). arXiv:1512.03012 arXiv preprint"},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Chen, G., Wang, M., Yang, Y., Yu, K., Yuan, L., Yue, Y.: PointGPT: auto-regressively generative pre-training from point clouds. Adv. Neural Inform. Process. Syst. 36 (2024)","DOI":"10.52202\/075280-1291"},{"key":"32_CR7","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607 (2020)"},{"issue":"12","key":"32_CR8","doi-asserted-by":"publisher","first-page":"12158","DOI":"10.1109\/TCSVT.2024.3430904","volume":"34","author":"H Cheng","year":"2024","unstructured":"Cheng, H., Zhu, J., Hu, N., Chen, J., Yan, W.: PTM: torus masking for 3D representation learning guided by robust and trusted teachers. IEEE Trans. Circuit Syst. Video Technol. 34(12), 12158\u201312170 (2024)","journal-title":"IEEE Trans. Circuit Syst. Video Technol."},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Choy, C., Gwak, J., Savarese, S.: 4D spatio-temporal convnets: minkowski convolutional neural networks. In: IEEE IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3075\u20133084 (2019)","DOI":"10.1109\/CVPR.2019.00319"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Deng, Z., Li, X., Li, X., Tong, Y., Zhao, S., Liu, M.: VG4D: vision-language model goes 4D video recognition. In: IEEE International Conference on Robotics and Automation, pp. 5014\u20135020. IEEE (2024)","DOI":"10.1109\/ICRA57147.2024.10610217"},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Fan, H., Yang, Y., Kankanhalli, M.: Point 4d transformer networks for spatio-temporal modeling in point cloud videos. In: IEEE International Conference on Robotics and Automation (2021)","DOI":"10.1109\/CVPR46437.2021.01398"},{"issue":"2","key":"32_CR12","doi-asserted-by":"publisher","first-page":"2181","DOI":"10.1109\/TPAMI.2022.3161735","volume":"45","author":"H Fan","year":"2023","unstructured":"Fan, H., Yang, Y., Kankanhalli, M.: Point spatio-temporal transformer networks for point cloud video modeling. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 2181\u20132192 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"32_CR13","unstructured":"Fan, H., Yu, X., Ding, Y., Yang, Y., Kankanhalli, M.: PSTNet: point spatio-temporal convolution on point cloud sequences. In: International Conference on Learning Representations (2021)"},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Fu, M., Zhu, K., Wu, J.: DTL: disentangled transfer learning for visual recognition. In: AAAI 38, 12082\u201312090 (2024)","DOI":"10.1609\/aaai.v38i11.29096"},{"key":"32_CR15","unstructured":"Guo, Z., et al.: Mantis: mamba-native tuning is efficient for 3D point cloud foundation models (2026). arXiv:2605.03438 arXiv preprint"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Guo, Z., Zhang, R., Qiu, L., Li, X., Heng, P.A.: Joint-MAE: 2D-3D joint masked autoencoders for 3d point cloud pre-training. In: IJCAI, pp. 791\u2013799 (2023)","DOI":"10.24963\/ijcai.2023\/88"},{"key":"32_CR17","doi-asserted-by":"crossref","unstructured":"Han, X., Sun, Y., Lu, C.: Rethinking regressor in 3D Gaussian pretraining. In: Pattern Recognition and Computer Vision - Springer Nature (2025)","DOI":"10.1007\/978-981-95-5737-0_13"},{"key":"32_CR18","doi-asserted-by":"crossref","unstructured":"Han, Y., Xu, C., Xu, R., Qian, J., Xie, J.: Masked motion prediction with semantic contrast for point cloud sequence learning. In: European Conference on Computer Vision, pp. 414\u2013431. Springer (2024)","DOI":"10.1007\/978-3-031-73116-7_24"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"issue":"2","key":"32_CR20","first-page":"3","volume":"1","author":"EJ Hu","year":"2022","unstructured":"Hu, E.J., et al.: LORA: low-rank adaptation of large language models. Int. Conf. Learn. Represent. 1(2), 3 (2022)","journal-title":"Int. Conf. Learn. Represent."},{"key":"32_CR21","doi-asserted-by":"crossref","unstructured":"Jing, L., et al.: X4D-SceneFormer: enhanced scene understanding on 4D point cloud videos through cross-modal knowledge transfer. In: AAAI, vol.\u00a038, pp. 2670\u20132678 (2024)","DOI":"10.1609\/aaai.v38i3.28045"},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Li, P., Sun, Y., Cheng, H.: PointDico: contrastive 3D representation learning guided by diffusion models. In: IEEE International Joint Conference on Neural Networks (2025)","DOI":"10.1109\/IJCNN64981.2025.11227674"},{"key":"32_CR23","doi-asserted-by":"crossref","unstructured":"Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 9\u201314 (2010)","DOI":"10.1109\/CVPRW.2010.5543273"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Liang, D., Feng, T., Zhou, X., Zhang, Y., Zou, Z., Bai, X.: Parameter-efficient fine-tuning in spectral domain for point cloud learning. IEEE Trans. Pattern Anal. Mach. Intell. 47(12) (2025)","DOI":"10.1109\/TPAMI.2025.3594749"},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Mamba4D: efficient 4D point cloud video understanding with disentangled spatial-temporal state space models. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 17626\u201317636 (2025)","DOI":"10.1109\/CVPR52734.2025.01642"},{"key":"32_CR26","doi-asserted-by":"crossref","unstructured":"Liu, X., Yan, M., Bohg, J.: MeteorNet: deep learning on dynamic 3D point cloud sequences. In: International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00934"},{"key":"32_CR27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, C., Wang, C., King, X., Liu, M.: Regress before construct: regress autoencoder for point cloud self-supervised learning. In: ACM International Conference Multimedia, pp. 1738\u20131749 (2023)","DOI":"10.1145\/3581783.3612106"},{"key":"32_CR28","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, C., Wang, Z., Yi, L.: CrossVideo: self-supervised cross-modal contrastive learning for point cloud video understanding. In: IEEE International Conference on Robotics and Automation, pp. 12436\u201312442 (2024)","DOI":"10.1109\/ICRA57147.2024.10610376"},{"key":"32_CR29","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, J., Zhang, Z., Huang, J., Yi, L.: LeaF: learning frames for 4D point cloud sequence understanding. In: International Conference on Computer Vision, pp. 604\u2013613. (2023)","DOI":"10.1109\/ICCV51070.2023.00062"},{"key":"32_CR30","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: HOI4D: a 4D egocentric dataset for category-level human-object interaction. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 21013\u201321022 (2022)","DOI":"10.1109\/CVPR52688.2022.02034"},{"key":"32_CR31","doi-asserted-by":"crossref","unstructured":"Lv, B., Zha, Y., Dai, T., Yuerong, X., Chen, K., Xia, S.T.: Adapting pre-trained 3D models for point cloud video understanding via cross-frame spatio-temporal perception. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 12413\u201312422 (2025)","DOI":"10.1109\/CVPR52734.2025.01158"},{"key":"32_CR32","doi-asserted-by":"crossref","unstructured":"Min, Y., Zhang, Y., Chai, X., Chen, X.: An efficient pointLSTM for point clouds based gesture recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5760\u20135769 (2020)","DOI":"10.1109\/CVPR42600.2020.00580"},{"key":"32_CR33","doi-asserted-by":"crossref","unstructured":"Pang, Y., Wang, W., Tay, F.E., Liu, W., Tian, Y., Yuan, L.: Masked autoencoders for point cloud self-supervised learning. In: European Conference on Computer Vision, pp. 604\u2013621 (2022)","DOI":"10.1007\/978-3-031-20086-1_35"},{"key":"32_CR34","unstructured":"Park, S., Kwak, N.: Feature-level ensemble knowledge distillation for aggregating knowledge from multiple networks. In: ECAI (2020)"},{"key":"32_CR35","unstructured":"Qi, Z., et al.: Contrast with reconstruct: contrastive 3D representation learning guided by generative pretraining. In: International Conference on Machine Learning (2023)"},{"key":"32_CR36","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763 (2021)"},{"key":"32_CR37","unstructured":"Rudin, W.: Principles of Mathematical Analysis, McGraw-Hill (1976). 3 edn"},{"key":"32_CR38","unstructured":"Shao, M., et al.: UniDef: universal defense against unauthorized image manipulation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition , pp. 8631\u20138640 (2026)"},{"key":"32_CR39","unstructured":"Shao, M., et al.: Wavelet-driven 3D anomaly detection under pose-agnostic and sparse-view. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition , pp. 43083\u201343092 (2026)"},{"key":"32_CR40","doi-asserted-by":"crossref","unstructured":"Shen, Z., et al.: Masked spatio-temporal structure prediction for self-supervised learning on point cloud videos. In: International Conference on Computer Vision, pp. 16580\u201316589 (2023)","DOI":"10.1109\/ICCV51070.2023.01520"},{"key":"32_CR41","doi-asserted-by":"crossref","unstructured":"Shen, Z., Sheng, X., Wang, L., Guo, Y., Liu, Q., Zhou, X.: PointCMP: contrastive mask prediction for self-supervised learning on point cloud videos. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1212\u20131222 (2023)","DOI":"10.1109\/CVPR52729.2023.00123"},{"key":"32_CR42","doi-asserted-by":"crossref","unstructured":"Sheng, X., Shen, Z., Xiao, G.: Contrastive predictive autoencoders for dynamic point cloud self-supervised learning. In: AAAI , vol 37, pp. 9802\u20139810 (2023)","DOI":"10.1609\/aaai.v37i8.26170"},{"key":"32_CR43","doi-asserted-by":"crossref","unstructured":"Sheng, X., Shen, Z., Xiao, G., Wang, L., Guo, Y., Fan, H.: Point contrastive prediction with semantic clustering for self-supervised learning on point cloud videos. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 16515\u201316524 (2023)","DOI":"10.1109\/ICCV51070.2023.01514"},{"key":"32_CR44","unstructured":"de\u00a0Smedt, Q., Wannous, H., Vandeborre, J.P., Guerry, J., Le\u00a0Saux, B., Filliat, D.: SHREC\u201917 Track: 3D hand gesture recognition using a depth and skeletal dataset. In: Proceedings of 10th Eurographics Workshop on 3D Object Retrieval, pp.\u00a01\u20136 (2017)"},{"key":"32_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.112800","volume":"173","author":"Y Sun","year":"2026","unstructured":"Sun, Y., et al.: HyperPoint: multimodal 3D foundation model in hyperbolic space. Pattern Recogn. 173, 112800 (2026)","journal-title":"Pattern Recogn."},{"key":"32_CR46","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zhu, J., Cheng, H., Lu, C., Yang, Z., Chen, L., Wang, Y.: Align then adapt: rethinking parameter-efficient transfer learning in 4D perception. In: IEEE Transation Multimedia (2026)","DOI":"10.1109\/TMM.2026.3705202"},{"key":"32_CR47","doi-asserted-by":"publisher","first-page":"12991","DOI":"10.52202\/068431-0944","volume":"35","author":"YL Sung","year":"2022","unstructured":"Sung, Y.L., Cho, J., Bansal, M.: LST: ladder side-tuning for parameter and memory efficient transfer learning. Adv. Neural Inform. Process. Syst. 35, 12991\u201313005 (2022)","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"32_CR48","doi-asserted-by":"crossref","unstructured":"Tang, Y., et al.: Point-PEFT: parameter-efficient fine-tuning for 3D pre-trained models. In: AAAI, vol.\u00a038, pp. 5171\u20135179 (2024)","DOI":"10.1609\/aaai.v38i6.28323"},{"key":"32_CR49","doi-asserted-by":"crossref","unstructured":"Uy, M.A., Pham, Q.H., Hua, B.S., Nguyen, T., Yeung, S.K.: Revisiting point cloud classification: a new benchmark dataset and classification model on real-world data. In: International Conference on Computer Vision, pp. 1588\u20131597 (2019)","DOI":"10.1109\/ICCV.2019.00167"},{"key":"32_CR50","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (2017)"},{"key":"32_CR51","doi-asserted-by":"crossref","unstructured":"Wang, J., Lin, C., Nie, L., Liao, K., Shao, S., Zhao, Y.: Digging into contrastive learning for robust depth estimation with diffusion models. In: Proceedings of the 32nd ACM International Conference on Multimedia, pp. 4129\u20134137. MM \u201924, ACM (2024)","DOI":"10.1145\/3664647.3681168"},{"key":"32_CR52","unstructured":"Wang, J., et al.: Geometry-guided reinforcement learning for multi-view consistent 3D scene editing (2026). arXiv:2603.03143 arXiv preprint"},{"key":"32_CR53","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: PointLORA: low-rank adaptation with token selection for point cloud learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6605\u20136615 (2025)","DOI":"10.1109\/CVPR52734.2025.00619"},{"key":"32_CR54","unstructured":"Wang, Y., Sun, Y., Wang, Q., Li, P., Lu, C., Zhang, D.: PointRFT: explicit reinforcement fine-tuning for point cloud few-shot learning. In: IEEE International Conference on Multimedia and Expo (ICME) (2026)"},{"key":"32_CR55","doi-asserted-by":"crossref","unstructured":"Wei, L., Lu, J., Cheng, H., Zhu, J., Zhang, K.: Point-SRA: self-representation alignment for 3D representation learning. In: AAAI (2026)","DOI":"10.1609\/aaai.v40i13.38026"},{"key":"32_CR56","doi-asserted-by":"crossref","unstructured":"Wen, H., Liu, Y., Huang, J., Duan, B., Yi, L.: Point primitive transformer for long-term 4D point cloud video understanding. In: European Conference on Computer Vision, pp. 19\u201335. Springer (2022)","DOI":"10.1007\/978-3-031-19818-2_2"},{"key":"32_CR57","doi-asserted-by":"crossref","unstructured":"Xie, S., Gu, J., Guo, D., Qi, C.R., Litany, L.G.O.: PointContrast: unsupervised pre-training for 3D point cloud understanding. In: European Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-58580-8_34"},{"key":"32_CR58","doi-asserted-by":"crossref","unstructured":"Yang, W., Wei, Q., Ma, C., Tan, W., Yan, B.: Scaling laws for data-efficient visual transfer learning. In: ACM International Conference Multimedia, pp. 2968\u20132976 (2025)","DOI":"10.1145\/3746027.3754916"},{"key":"32_CR59","doi-asserted-by":"crossref","unstructured":"Yu, X., Tang, L., Rao, Y., Huang, T., Zhou, J., Lu, J.: Point-BERT: pre-training 3D point cloud transformers with masked point modeling. In: IEEE : IEEE Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01871"},{"key":"32_CR60","doi-asserted-by":"crossref","unstructured":"Zha, Y., Wang, J., Dai, T., Chen, B., Wang, Z., Xia, S.T.: Instance-aware dynamic prompt tuning for pre-trained point cloud models. In: International Conference on Computer Vision, pp. 14161\u201314170 (2023)","DOI":"10.1109\/ICCV51070.2023.01302"},{"key":"32_CR61","doi-asserted-by":"crossref","unstructured":"Zha, Y., et al.: PMA: towards parameter-efficient point cloud understanding via point mamba adapter. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 16976\u201316986 (2025)","DOI":"10.1109\/CVPR52734.2025.01582"},{"key":"32_CR62","unstructured":"Zhang, D., et\u00a0al.: PointCot: a multi-modal benchmark for explicit 3D geometric reasoning. arXiv preprint arXiv:2602.23945 (2026)"},{"key":"32_CR63","doi-asserted-by":"crossref","unstructured":"Zhang, D., Wang, Y., Sun, Y., Xu, H., Fan, P., Zhu, J.: CMHANet: a cross-modal hybrid attention network for point cloud registration. Neurocomputing (2026)","DOI":"10.1016\/j.neucom.2026.133318"},{"key":"32_CR64","doi-asserted-by":"crossref","unstructured":"Zhang, R., et al.: Point-M2AE: multi-scale masked autoencoders for hierarchical point cloud pre-training. Adv. Neural Inform. Process. Syst. (2022)","DOI":"10.52202\/068431-1962"},{"key":"32_CR65","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Dong, Y., Liu, Y., Yi, L.: Complete-to-partial 4D distillation for self-supervised point cloud sequence representation learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 17661\u201317670 (2023)","DOI":"10.1109\/CVPR52729.2023.01694"},{"key":"32_CR66","unstructured":"Zhang, Z., et al.: Diffusion masked pretraining for dynamic point cloud (2026). arXiv:2605.03639 arXiv preprint"},{"key":"32_CR67","doi-asserted-by":"crossref","unstructured":"Zheng, X., et al.: Point cloud pre-training with diffusion models. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 22935\u201322945 (2024)","DOI":"10.1109\/CVPR52733.2024.02164"},{"key":"32_CR68","doi-asserted-by":"crossref","unstructured":"Zhong, J.X., Zhou, K., Hu, Q., Wang, B., Trigoni, N., Markham, A.: No pain, big gain: classify dynamic point cloud sequences with static models by fitting feature-level space-time surfaces. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8510\u20138520 (2022)","DOI":"10.1109\/CVPR52688.2022.00832"},{"key":"32_CR69","doi-asserted-by":"crossref","unstructured":"Zhou, X., et al.: Dynamic adapter meets prompt tuning: parameter-efficient transfer learning for point cloud analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 14707\u201314717 (2024)","DOI":"10.1109\/CVPR52733.2024.01393"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37038-9_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:37:35Z","timestamp":1789123055000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37038-9_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032370372","9783032370389"],"references-count":69,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37038-9_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"12 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}