{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:14:32Z","timestamp":1760145272226,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62273034","U1805264","62376269","BK21BF004"],"award-info":[{"award-number":["62273034","U1805264","62376269","BK21BF004"]}]},{"name":"Scientific and Technological Innovation Foundation of Foshan","award":["62273034","U1805264","62376269","BK21BF004"],"award-info":[{"award-number":["62273034","U1805264","62376269","BK21BF004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this work, a language-level Semantics-Conditioned framework for 3D Point cloud segmentation, called SeCondPoint, is proposed, where language-level semantics are introduced to condition the modeling of the point feature distribution, as well as the pseudo-feature generation, and a feature\u2013geometry-based Mixup approach is further proposed to facilitate the distribution learning. Since a large number of point features could be generated from the learned distribution thanks to the semantics-conditioned modeling, any existing segmentation network could be embedded into the proposed framework to boost its performance. In addition, the proposed framework has the inherent advantage of dealing with novel classes, which seems an impossible feat for the current segmentation networks. Extensive experimental results on two public datasets demonstrate that three typical segmentation networks could achieve significant improvements over their original performances after enhancement by the proposed framework in the conventional 3D segmentation task. Two benchmarks are also introduced for a newly introduced zero-shot 3D segmentation task, and the results also validate the proposed framework.<\/jats:p>","DOI":"10.3390\/rs16132376","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T08:31:36Z","timestamp":1719563496000},"page":"2376","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Language-Level Semantics-Conditioned 3D Point Cloud Segmentation"],"prefix":"10.3390","volume":"16","author":[{"given":"Bo","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4137-7424","authenticated-orcid":false,"given":"Hui","family":"Zeng","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4015-1615","authenticated-orcid":false,"given":"Qiulei","family":"Dong","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Zhanyi","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Choy, C., Gwak, J., and Savarese, S. (2019, January 15\u201320). 4d spatio-temporal convnets: Minkowski convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00319"},{"key":"ref_2","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_3","unstructured":"Yang, Y.-Q., Guo, Y.-X., Xiong, J.-Y., Liu, Y., Pan, H., Wang, P.-S., Tong, X., and Guo, B. (2023). Swin3d: A pretrained transformer backbone for 3d indoor scene understanding. arXiv."},{"key":"ref_4","unstructured":"Maturana, D., and Scherer, S. (October, January 28). Voxnet: A 3d convolutional neural network for real-time object recognition. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Hamburg, Germany."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lawin, F.J., Danelljan, M., Tosteberg, P., Bhat, G., Khan, F.S., and Felsberg, M. (2017, January 22\u201324). Deep projective 3d semantic segmentation. Proceedings of the Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden.","DOI":"10.1007\/978-3-319-64689-3_8"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wu, B., Wan, A., Yue, X., and Keutzer, K. (2018, January 21\u201325). Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud. Proceedings of the IEEE International Conference on Robotics and Automation, Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8462926"},{"key":"ref_7","unstructured":"Meng, H.-Y., Gao, L., Lai, Y., and Manocha, D. (November, January 27). Vv-net: Voxel vae net with group convolutions for point cloud segmentation. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_8","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017, January 4\u20139). Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Proceedings of the Conference on Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, A., and Markham, A. (2020, January 13\u201319). Randla-net: Efficient semantic segmentation of large-scale point clouds. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref_10","unstructured":"Qiu, H., Yu, B., Chen, Y., and Tao, D. (2023). Pointhr: Exploring high-resolution architectures for 3D point cloud segmentation. arXiv."},{"key":"ref_11","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.-E., Marcotegui, B., Goulette, F., and Guibas, L.J. (November, January 27). Kpconv: Flexible and deformable convolution for point clouds. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_12","unstructured":"Engelmann, F., Kontogianni, T., and Leibe, B. (August, January 31). Dilated point convolutions: On the receptive field size of point convolutions on 3d point clouds. Proceedings of the IEEE International Conference on Robotics and Automation, Paris, France."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Peng, B., Wu, X., Jiang, L., Chen, Y., Zhao, H., Tian, Z., and Jia, J. (2024). Oa-cnns: Omni-adaptive sparse cnns for 3d semantic segmentation. arXiv.","DOI":"10.1109\/CVPR52733.2024.02013"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3618331","article-title":"Octformer: Octree-based transformers for 3d point clouds","volume":"42","author":"Wang","year":"2023","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, R., Li, X., Heng, P.-A., and Fu, C.-W. (2020, January 14\u201319). Pointaugment: An auto-augmentation framework for point cloud classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00641"},{"key":"ref_16","unstructured":"Zhang, H., Ciss\u00e9, M., Dauphin, Y.N., and Lopez-Paz, D. (May, January 30). Mixup: Beyond empirical risk minimization. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, Y., Hu, V.T., Gavves, E., Mensink, T., Mettes, P., Yang, P., and Snoek, C.G. (2020, January 23\u201328). Pointmixup: Augmentation for point clouds. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58580-8_20"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lee, D., Lee, J., Lee, J., Lee, H., Lee, M., Woo, S., and Lee, S. (2021, January 19\u201325). Regularization strategy for point cloud via rigidly mixed sample. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01564"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nekrasov, A., Schult, J., Litany, O., Leibe, B., and Engelmann, F. (2021, January 1\u20133). Mix3d: Out-of-context data augmentation for 3d scenes. Proceedings of the 2021 International Conference on 3D Vision (3DV), Online.","DOI":"10.1109\/3DV53792.2021.00022"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105049","DOI":"10.1016\/j.imavis.2024.105049","article-title":"VAE-GAN3D: Leveraging image-based semantics for 3D zero-shot recognition","volume":"147","author":"Abdullah","year":"2024","journal-title":"Image Vis. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109843","DOI":"10.1016\/j.patcog.2023.109843","article-title":"Contrastive generative network with recursive-loop for 3D point cloud generalized zero-shot classification","volume":"144","author":"Hao","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cheraghian, A., Rahman, S., Campbell, D., and Petersson, L. (2020, January 1\u20135). Transductive zero-shot learning for 3D point cloud classification. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093545"},{"key":"ref_23","unstructured":"Wang, Y., Huang, S., Gao, Y., Wang, Z., Wang, R., Sheng, K., Zhang, B., and Liu, S. (November, January 29). Transferring clip\u2019s knowledge into zero-shot point cloud semantic segmentation. Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, ON, Canada."},{"key":"ref_24","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., and Clark, J. (2021, January 18\u201324). Learning transferable visual models from natural language supervision. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_25","unstructured":"Chen, R., Zhu, X., Chen, N., Li, W., Ma, Y., Yang, R., and Wang, W. (November, January 29). Bridging language and geometric primitives for zero-shot point cloud segmentation. Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, ON, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3199","DOI":"10.1109\/TIP.2023.3279660","article-title":"Prototype adaption and projection for few-and zero-shot 3d point cloud semantic segmentation","volume":"32","author":"He","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Graham, B., Engelcke, M., and van der Maaten, L. (2018, January 18\u201322). 3D semantic segmentation with submanifold sparse convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00961"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jiang, M., Wu, Y., Zhao, T., Zhao, Z., and Lu, C. (2018). Pointsift: A sift-like network module for 3d point cloud semantic segmentation. arXiv.","DOI":"10.1109\/IGARSS.2019.8900102"},{"key":"ref_29","unstructured":"Jiang, L., Zhao, H., Liu, S., Shen, X., Fu, C., and Jia, J. (November, January 27). Hierarchical point-edge interaction network for point cloud semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhang, Q., Ni, B., Li, L., Liu, J., Zhou, M., and Tian, Q. (2019, January 15\u201320). Modeling point clouds with self-attention and gumbel subset sampling. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00344"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Han, W., Wen, C., Wang, C., Li, X., and Li, Q. (2020, January 7\u201312). Point2node: Correlation learning of dynamic-node for point cloud feature modeling. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6725"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ma, Y., Guo, Y., Liu, H., Lei, Y., and Wen, G.-J. (2020, January 1\u20135). Global context reasoning for semantic segmentation of 3d point clouds. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093411"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cheng, M., Hui, L., Xie, J., Yang, J., and Kong, H. (2020\u201324, January 24). Cascaded non-local neural network for point cloud semantic segmentation. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341531"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Deng, S., Liu, B., Dong, Q., and Hu, Z. (2021, January 5\u20139). Rotation transformation network: Learning view-invariant point cloud for classification and segmentation. Proceedings of the IEEE International Conference on Multimedia and Expo, Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428265"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Deng, S., Dong, Q., Liu, B., and Hu, Z. (2021). Superpoint-guided semi-supervised semantic segmentation of 3d point clouds. arXiv.","DOI":"10.1109\/ICRA46639.2022.9811904"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fan, S., Dong, Q., Zhu, F., Lv, Y., Ye, P., and Wang, F.-Y. (2021, January 19\u201325). Scf-net: Learning spatial contextual features for large-scale point cloud segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01427"},{"key":"ref_37","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., and Chen, B. (2020, January 6\u201312). Pointcnn: Convolution on x-transformed points. Proceedings of the Conference on Advances in Neural Information Processing Systems, Online."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, S., Suo, S., Ma, W.-C., Pokrovsky, A., and Urtasun, R. (2018, January 18\u201322). Deep parametric continuous convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00274"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xu, Y., Fan, T., Xu, M., Zeng, L., and Qiao, Y. (2018, January 8\u201314). Spidercnn: Deep learning on point sets with parameterized convolutional filters. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01237-3_6"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Komarichev, A., Zhong, Z., and Hua, J. (2019, January 15\u201320). A-CNN: Annularly convolutional neural networks on point clouds. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00760"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lei, H., Akhtar, N., and Mian, A. (2019, January 15\u201320). Octree guided cnn with spherical kernels for 3D point clouds. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00986"},{"key":"ref_42","unstructured":"Mao, J., Wang, X., and Li, H. (November, January 27). Interpolated convolutional networks for 3d point cloud understanding. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Landrieu, L., and Simonovsky, M. (2018, January 18\u201322). Large-scale point cloud semantic segmentation with superpoint graphs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00479"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Fu, C.-W., and Jia, J. (2019, January 15\u201320). Pointweb: Enhancing local neighborhood features for point cloud processing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00571"},{"key":"ref_45","first-page":"1","article-title":"Dynamic graph cnn for learning on point clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, L., Huang, Y., Hou, Y., Zhang, S., and Shan, J. (2019, January 15\u201320). Graph attention convolution for point cloud semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01054"},{"key":"ref_47","first-page":"33330","article-title":"Point transformer v2: Grouped vector attention and partition-based pooling","volume":"35","author":"Wu","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","article-title":"Zero-shot learning\u2014A comprehensive evaluation of the good, the bad and the ugly","volume":"41","author":"Xian","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","unstructured":"Liu, B., Dong, Q., and Hu, Z. (2021, January 19\u201325). Hardness sampling for self-training based transductive zero-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Xian, Y., Choudhury, S., He, Y., Schiele, B., and Akata, Z. (2019, January 15\u201320). Semantic projection network for zero- and few-label semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00845"},{"key":"ref_51","unstructured":"Bucher, M., Vu, T.-H., Cord, M., and P\u00e9rez, P. (2019, January 8\u201314). Zero-shot semantic segmentation. Proceedings of the Conference on Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Gu, Z., Zhou, S., Niu, L., Zhao, Z., and Zhang, L. (2020, January 12\u201316). Context-aware feature generation for zero-shot semantic segmentation. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","DOI":"10.1145\/3394171.3413593"},{"key":"ref_53","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013, January 5\u201310). Distributed representations of words and phrases and their compositionality. Proceedings of the Conference on Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C.D. (2014, January 25\u201329). Glove: Global vectors for word representation. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_55","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019, January 6\u201311). Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics, Online."},{"key":"ref_56","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein gan. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Armeni, I., Sener, O., Zamir, A., Jiang, H., Brilakis, I., Fischer, M., and Savarese, S. (2016, January 27\u201330). 3D semantic parsing of large-scale indoor spaces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.170"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., and Nie\u00dfner, M. (2017, January 21\u201326). Scannet: Richly-annotated 3d reconstructions of indoor scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.261"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"6943","DOI":"10.1109\/TIP.2021.3100552","article-title":"An iterative co-training transductive framework for zero shot learning","volume":"30","author":"Liu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_60","unstructured":"Frome, A., Corrado, G.S., Shlens, J., Bengio, S., Dean, J., Ranzato, M.A., and Mikolov, T. (2013, January 5\u201310). Devise: A deep visual-semantic embedding model. Proceedings of the Conference on Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Xian, Y., Lorenz, T., Schiele, B., and Akata, Z. (2018, January 18\u201322). Feature generating networks for zero-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00581"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2376\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:06:59Z","timestamp":1760108819000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2376"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":61,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16132376"],"URL":"https:\/\/doi.org\/10.3390\/rs16132376","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,6,28]]}}}