{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T15:38:06Z","timestamp":1774712286279,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Research Council Discovery Early Career Award","award":["DE230101058"],"award-info":[{"award-number":["DE230101058"]}]},{"name":"Australian Research Council Discovery Early Career Award","award":["FT210100268"],"award-info":[{"award-number":["FT210100268"]}]},{"name":"Australian Government","award":["DE230101058"],"award-info":[{"award-number":["DE230101058"]}]},{"name":"Australian Government","award":["FT210100268"],"award-info":[{"award-number":["FT210100268"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Semantic scene completion is a crucial outdoor scene understanding task that has direct implications for technologies like autonomous driving and robotics. It compensates for unavoidable occlusions and partial measurements in LiDAR scans, which may otherwise cause catastrophic failures. Due to the inherent complexity of this task, existing methods generally rely on complex and computationally demanding scene completion models, which limits their practicality in downstream applications. Addressing this, we propose a novel integrated network that combines the strengths of 3D and 2D semantic scene completion techniques for efficient LiDAR point cloud scene completion. Our network leverages a newly devised lightweight multi-scale convolutional block (MSB) to efficiently aggregate multi-scale features, thereby improving the identification of small and distant objects. It further utilizes a layout-aware semantic block (LSB), developed to grasp the overall layout of the scene to precisely guide the reconstruction and recognition of features. Moreover, we also develop a feature fusion module (FFM) for effective interaction between the data derived from two disparate streams in our network, ensuring a robust and cohesive scene completion process. Extensive experiments with the popular SemanticKITTI dataset demonstrate that our method achieves highly competitive performance, with an mIoU of 35.7 and an IoU of 51.4. Notably, the proposed method achieves an mIoU improvement of 2.6 % compared to previous methods.<\/jats:p>","DOI":"10.3390\/rs16132266","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T08:50:08Z","timestamp":1718959808000},"page":"2266","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Voxel- and Bird\u2019s-Eye-View-Based Semantic Scene Completion for LiDAR Point Clouds"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3127-9599","authenticated-orcid":false,"given":"Li","family":"Liang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3406-673X","authenticated-orcid":false,"given":"Naveed","family":"Akhtar","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, The University of Melbourne, Parkville, VIC 3052, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3951-1188","authenticated-orcid":false,"given":"Jordan","family":"Vice","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5206-3842","authenticated-orcid":false,"given":"Ajmal","family":"Mian","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M., and Funkhouser, T. (2017, January 21\u201326). Semantic scene completion from a single depth image. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.28"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Roldao, L., de Charette, R., and Verroust-Blondet, A. (2020, January 25\u201328). Lmscnet: Lightweight multiscale 3d semantic completion. Proceedings of the 2020 International Conference on 3D Vision (3DV), Fukuoka, Japan.","DOI":"10.1109\/3DV50981.2020.00021"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yan, X., Gao, J., Li, J., Zhang, R., Li, Z., Huang, R., and Cui, S. (2021, January 2\u20139). Sparse single sweep lidar point cloud segmentation via learning contextual shape priors from scene completion. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v35i4.16419"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cheng, R., Agia, C., Ren, Y., Li, X., and Bingbing, L. (2021, January 8\u201311). S3cnet: A sparse semantic scene completion network for lidar point clouds. Proceedings of the Conference on Robot Learning, PMLR, London, UK.","DOI":"10.1109\/ICRA48506.2021.9561305"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yang, X., Zou, H., Kong, X., Huang, T., Liu, Y., Li, W., Wen, F., and Zhang, H. (October, January 27). Semantic segmentation-assisted scene completion for lidar point clouds. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636662"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xia, Z., Liu, Y., Li, X., Zhu, X., Ma, Y., Li, Y., Hou, Y., and Qiao, Y. (2023, January 18\u201319). SCPNet: Semantic Scene Completion on Point Cloud. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01692"},{"key":"ref_7","unstructured":"Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., and Gall, J. (November, January 29). Semantickitti: A dataset for semantic scene understanding of lidar sequences. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Guo, Y.X., and Tong, X. (2018). View-volume network for semantic scene completion from a single depth image. arXiv.","DOI":"10.24963\/ijcai.2018\/101"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, Y., Tan, D.J., Navab, N., and Tombari, F. (2018, January 5\u20138). Adversarial semantic scene completion from a single depth image. Proceedings of the 2018 International Conference on 3D Vision (3DV), Verona, Italy.","DOI":"10.1109\/3DV.2018.00056"},{"key":"ref_10","unstructured":"Wang, Y., Tan, D.J., Navab, N., and Tombari, F. (November, January 29). Forknet: Multi-branch volumetric semantic completion from a single depth image. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_11","unstructured":"Zhang, P., Liu, W., Lei, Y., Lu, H., and Yang, X. (November, January 29). Cascaded context pyramid for full-resolution 3d semantic scene completion. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dai, A., Diller, C., and Nie\u00dfner, M. (2020, January 16\u201318). Sg-nn: Sparse generative neural networks for self-supervised scene completion of rgb-d scans. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00093"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wu, S.C., Tateno, K., Navab, N., and Tombari, F. (2020, January 25\u201328). Scfusion: Real-time incremental scene reconstruction with semantic completion. Proceedings of the 2020 International Conference on 3D Vision (3DV), Fukuoka, Japan.","DOI":"10.1109\/3DV50981.2020.00090"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cao, A.Q., and de Charette, R. (2022, January 18\u201324). Monoscene: Monocular 3d semantic scene completion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00396"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, Y., Yu, Z., Choy, C., Xiao, C., Alvarez, J.M., Fidler, S., Feng, C., and Anandkumar, A. (2023, January 17\u201324). Voxformer: Sparse voxel transformer for camera-based 3d semantic scene completion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00877"},{"key":"ref_16","unstructured":"Li, B., Sun, Y., Jin, X., Zeng, W., Zhu, Z., Wang, X., Zhang, Y., Okae, J., Xiao, H., and Du, D. (2023). StereoScene: BEV-Assisted Stereo Matching Empowers 3D Semantic Scene Completion. arXiv."},{"key":"ref_17","unstructured":"Jiang, H., Cheng, T., Gao, N., Zhang, H., Liu, W., and Wang, X. (2023). Symphonize 3D Semantic Scene Completion with Contextual Instance Queries. arXiv."},{"key":"ref_18","unstructured":"Miao, R., Liu, W., Chen, M., Gong, Z., Xu, W., Hu, C., and Zhou, S. (2023). Occdepth: A depth-aware method for 3d semantic scene completion. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hayler, A., Wimbauer, F., Muhle, D., Rupprecht, C., and Cremers, D. (2023). S4C: Self-Supervised Semantic Scene Completion with Neural Fields. arXiv.","DOI":"10.1109\/3DV62453.2024.00133"},{"key":"ref_20","unstructured":"Mei, J., Yang, Y., Wang, M., Zhu, J., Zhao, X., Ra, J., Li, L., and Liu, Y. (2023). Camera-based 3D Semantic Scene Completion with Sparse Guidance Network. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rist, C.B., Schmidt, D., Enzweiler, M., and Gavrila, D.M. (November, January 19). Scssnet: Learning spatially-conditioned scene segmentation on lidar point clouds. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.","DOI":"10.1109\/IV47402.2020.9304824"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, P.S., Liu, Y., and Tong, X. (2020, January 14\u201319). Deep octree-based CNNs with output-guided skip connections for 3D shape and scene completion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00141"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Nie, Y., Hou, J., Han, X., and Nie\u00dfner, M. (2021, January 20\u201325). Rfd-net: Point scene understanding by semantic instance reconstruction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00458"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, S., Hao, A., and Qin, H. (2021, January 2\u20139). Point cloud semantic scene completion from rgb-d images. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v35i4.16451"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7205","DOI":"10.1109\/TPAMI.2021.3095302","article-title":"Semantic scene completion using local deep implicit functions on lidar data","volume":"44","author":"Rist","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xiong, Y., Ma, W.C., Wang, J., and Urtasun, R. (2023, January 17\u201324). Learning Compact Representations for LiDAR Completion and Generation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00110"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, J., Li, X., Tang, Y., Yu, Q., Hao, Y., Hu, L., and Chen, M. (2023, January 7\u201314). Casfusionnet: A cascaded network for point cloud semantic scene completion by dense feature fusion. Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA.","DOI":"10.1609\/aaai.v37i3.25405"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, H., Dong, J., Wen, B., Gao, M., Huang, T., Liu, Y.H., and Cremers, D. (2023, January 1\u20136). DDIT: Semantic Scene Completion via Deformable Deep Implicit Templates. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.02001"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhao, H., Yao, A., Chen, Y., Zhang, L., and Liao, H. (2018, January 8\u201314). Efficient semantic scene completion network with spatial group convolution. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01258-8_45"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dai, A., Ritchie, D., Bokeloh, M., Reed, S., Sturm, J., and Nie\u00dfner, M. (2018, January 18\u201323). Scancomplete: Large-scale scene completion and semantic segmentation for 3d scans. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00481"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zou, H., Yang, X., Huang, T., Zhang, C., Liu, Y., Li, W., Wen, F., and Zhang, H. (October, January 27). Up-to-Down Network: Fusing Multi-Scale Context for 3D Semantic Scene Completion. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9635888"},{"key":"ref_32","unstructured":"Li, P., Shi, Y., Liu, T., Zhao, H., Zhou, G., and Zhang, Y.Q. (2021). Semi-supervised implicit scene completion from sparse LiDAR. arXiv."},{"key":"ref_33","first-page":"261","article-title":"See and think: Disentangling semantic scene completion","volume":"31","author":"Liu","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","unstructured":"Guedes, A.B.S., de Campos, T.E., and Hilton, A. (2018). Semantic scene completion combining colour and depth: Preliminary experiments. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/LRA.2019.2953639","article-title":"Depth based semantic scene completion with position importance aware loss","volume":"5","author":"Li","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Garbade, M., Chen, Y.T., Sawatzky, J., and Gall, J. (2019, January 16\u201317). Two stream 3d semantic scene completion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00055"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, J., Liu, Y., Gong, D., Shi, Q., Yuan, X., Zhao, C., and Reid, I. (2019, January 15\u201320). Rgbd based dimensional decomposition residual network for 3d semantic scene completion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00788"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, X., Lin, K.Y., Qian, C., Zeng, G., and Li, H. (2020, January 13\u201319). 3d sketch-aware semantic scene completion via semi-supervised structure prior. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00425"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, S., Zou, C., Li, Y., Zhao, X., and Gao, Y. (2020, January 7\u201312). Attention-based multi-modal fusion network for semantic scene completion. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6803"},{"key":"ref_40","unstructured":"Liu, Y., Li, J., Yan, Q., Yuan, X., Zhao, C., Reid, I., and Cadena, C. (2020). 3D gated recurrent fusion for semantic scene completion. arXiv."},{"key":"ref_41","first-page":"8125","article-title":"Anisotropic convolutional neural networks for RGB-D based semantic scene completion","volume":"44","author":"Li","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Cai, Y., Chen, X., Zhang, C., Lin, K.Y., Wang, X., and Li, H. (2021, January 20\u201325). Semantic scene completion via integrating instances and scene in-the-loop. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00039"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, J., Ding, L., and Huang, R. (2021). Imenet: Joint 3d semantic scene completion and 2d semantic segmentation through iterative mutual enhancement. arXiv.","DOI":"10.24963\/ijcai.2021\/110"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Dourado, A., Guth, F., and de Campos, T. (2022, January 3\u20138). Data augmented 3d semantic scene completion with 2d segmentation priors. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00076"},{"key":"ref_45","unstructured":"Wang, X., Lin, D., and Wan, L. (March, January 22). Ffnet: Frequency fusion network for semantic scene completion. Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_46","unstructured":"Tang, J., Chen, X., Wang, J., and Zeng, G. (March, January 22). Not all voxels are equal: Semantic scene completion from the point-voxel perspective. Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Fu, R., Wu, H., Hao, M., and Miao, Y. (2022, January 23\u201327). Semantic scene completion through multi-level feature fusion. Proceedings of the 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan.","DOI":"10.1109\/IROS47612.2022.9981517"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, F., Zhang, D., Zhang, H., Tang, J., and Sun, Q. (2023, January 17\u201324). Semantic Scene Completion with Cleaner Self. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00090"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Dong, H., Ma, E., Wang, L., Wang, M., Xie, W., Guo, Q., Li, P., Liang, L., Yang, K., and Lin, D. (2023, January 1\u20136). Cvsformer: Cross-view synthesis transformer for semantic scene completion. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00815"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Cao, H., and Behnke, S. (2024). SLCF-Net: Sequential LiDAR-Camera Fusion for Semantic Scene Completion using a 3D Recurrent U-Net. arXiv.","DOI":"10.1109\/ICRA57147.2024.10610602"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hou, Y., Zhu, X., Ma, Y., Loy, C.C., and Li, Y. (2022, January 21\u201324). Point-to-voxel knowledge distillation for lidar semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00829"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Tang, L., Zhan, Y., Chen, Z., Yu, B., and Tao, D. (2022, January 18\u201324). Contrastive boundary learning for point cloud segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00830"},{"key":"ref_53","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_54","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Li, J., Hassani, A., Walton, S., and Shi, H. (2023, January 17\u201324). Convmlp: Hierarchical convolutional mlps for vision. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00671"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Mei, J., Yang, Y., Wang, M., Huang, T., Yang, X., and Liu, Y. (2023, January 1\u20135). SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion. Proceedings of the 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA.","DOI":"10.1109\/IROS55552.2023.10341742"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2266\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:02:19Z","timestamp":1760108539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2266"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,21]]},"references-count":56,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16132266"],"URL":"https:\/\/doi.org\/10.3390\/rs16132266","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,21]]}}}