{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T09:55:11Z","timestamp":1776074111101,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Open-vocabulary 3D object detection (OV-3DOD) is crucial for real-world perception, yet existing monocular methods are often limited by predefined categories or heavy reliance on external 2D detectors. In this paper, we propose CLIP-Mono3D, an end-to-end one-stage transformer framework that directly integrates vision\u2013language semantics into monocular 3D detection. By leveraging CLIP-derived semantic priors and grounding object queries in semantically salient regions, our model achieves robust zero-shot generalization to novel categories without requiring auxiliary 2D detectors. Furthermore, we introduce OV-KITTI, a large-scale benchmark extending KITTI with 40 new categories and over 7000 annotated 3D bounding boxes. Extensive experiments on OV-KITTI, KITTI, and Argoverse demonstrate that CLIP-Mono3D achieves competitive performance in open-vocabulary scenarios.<\/jats:p>","DOI":"10.3390\/s26082380","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T08:49:02Z","timestamp":1776070142000},"page":"2380","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CLIP-Mono3D: End-to-End Open-Vocabulary Monocular 3D Object Detection via Semantic\u2013Geometric Similarity"],"prefix":"10.3390","volume":"26","author":[{"given":"Zichong","family":"Gu","sequence":"first","affiliation":[{"name":"School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5393-0258","authenticated-orcid":false,"given":"Shiyi","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanqi","family":"Lyu","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shugong","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Intelligent Science, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215123, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving? The kitti vision benchmark suite. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Song, S., Lichtenberg, S.P., and Xiao, J. (2015, January 1\u201312). Sun rgb-d: A rgb-d scene understanding benchmark suite. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298655"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., and Nie\u00dfner, M. (2017, January 22\u201325). Scannet: Richly-annotated 3D reconstructions of indoor scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.261"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020, January 13\u201319). Nuscenes: A multimodal dataset for autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_5","unstructured":"Yao, J., Gu, H., Chen, X., Wang, J., and Cheng, Z. (2024). Open Vocabulary Monocular 3D Object Detection. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pu, F., Wang, Y., Deng, J., and Yang, W. (2024). MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priors. arXiv.","DOI":"10.1109\/CVPR52734.2025.00611"},{"key":"ref_7","unstructured":"Xie, C., Wang, B., Kong, F., Li, J., Liang, D., Zhang, G., Leng, D., and Yin, Y. (2025). FG-CLIP: Fine-Grained Visual and Textual Alignment. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Qin, Z., and Li, X. (2022, January 18\u201324). Monoground: Detecting monocular 3D objects from the ground. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00377"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shi, X., Ye, Q., Chen, X., Chen, C., Chen, Z., and Kim, T.K. (2021, January 11\u201317). Geometry-based distance decomposition for monocular 3D object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01489"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wu, Z., and T\u00f3th, R. (2020, January 14\u201319). Smoke: Single-stage monocular 3D object detection via keypoint estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00506"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhu, X., Pang, J., and Lin, D. (2021, January 11\u201317). Fcos3d: Fully convolutional one-stage monocular 3D object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00107"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4593","DOI":"10.1109\/TITS.2023.3323036","article-title":"Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection","volume":"25","author":"Yao","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhou, D., Lu, F., Fang, J., and Zhang, L. (2021, January 11\u201317). Autoshape: Real-time shape-aware monocular 3D object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01535"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lu, Y., Ma, X., Yang, L., Zhang, T., Liu, Y., Chu, Q., Yan, J., and Ouyang, W. (2021, January 11\u201317). Geometry uncertainty projection network for monocular 3D object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00310"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, L., Yu, K., Tang, T., Li, J., Yuan, K., Wang, L., Zhang, X., and Chen, P. (2023, January 17\u201324). Bevheight: A robust framework for vision-based roadside 3D object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02070"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chao, W.L., Garg, D., Hariharan, B., Campbell, M., and Weinberger, K.Q. (2019, January 15\u201320). Pseudo-lidar from visual depth estimation: Bridging the gap in 3D object detection for autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00864"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ma, X., Liu, S., Xia, Z., Zhang, H., Zeng, X., and Ouyang, W. (2020, January 23\u201328). Rethinking pseudo-lidar representation. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-58601-0_19"},{"key":"ref_18","unstructured":"Chong, Z., Ma, X., Zhang, H., Yue, Y., Li, H., Wang, Z., and Ouyang, W. (2022). Monodistill: Learning spatial features for monocular 3D object detection. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rukhovich, D., Vorontsova, A., and Konushin, A. (2022, January 3\u20138). Imvoxelnet: Image to voxels projection for monocular and multi-view general-purpose 3D object detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00133"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Park, D., Ambrus, R., Guizilini, V., Li, J., and Gaidon, A. (2021, January 11\u201317). Is pseudo-lidar needed for monocular 3D object detection?. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00313"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, K.C., Wu, T.H., Su, H.T., and Hsu, W.H. (2022, January 18\u201324). Monodtr: Monocular 3D object detection with depth-aware transformer. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00398"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, R., Qiu, H., Wang, T., Guo, Z., Cui, Z., Qiao, Y., Li, H., and Gao, P. (2023, January 1\u20136). MonoDETR: Depth-guided transformer for monocular 3D object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00840"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, S., Lee, M., Lee, J., and Lee, S. (2026, January 20\u201327). MonoCLUE: Object-Aware Clustering Enhances Monocular 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, Singapore.","DOI":"10.1609\/aaai.v40i14.38157"},{"key":"ref_24","unstructured":"Ma, Z., Luo, G., Gao, J., Li, L., Chen, Y., Wang, S., Zhang, C., and Hu, W. (2022, January 18\u201324). Open-vocabulary one-stage detection with hierarchical visual-language knowledge distillation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhong, Y., Yang, J., Zhang, P., Li, C., Codella, N., Li, L.H., Zhou, L., Dai, X., Yuan, L., and Li, Y. (2022, January 18\u201324). Regionclip: Region-based language-image pretraining. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01629"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zareian, A., Rosa, K.D., Hu, D.H., and Chang, S.F. (2021, January 20\u201325). Open-vocabulary object detection using captions. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01416"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhou, X., Girdhar, R., Joulin, A., Kr\u00e4henb\u00fchl, P., and Misra, I. (2022, January 23\u201327). Detecting twenty-thousand classes using image-level supervision. Proceedings of the European Conference on Computer Vision (ECCV), Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-20077-9_21"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yao, L., Han, J., Liang, X., Xu, D., Zhang, W., Li, Z., and Xu, H. (2023, January 17\u201324). Detclipv2: Scalable open-vocabulary object detection pre-training via word-region alignment. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02250"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wu, X., Zhu, F., Zhao, R., and Li, H. (2023, January 17\u201324). Cora: Adapting clip for open-vocabulary detection with region prompting and anchor pre-matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00679"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zang, Y., Li, W., Zhou, K., Huang, C., and Loy, C.C. (2022, January 23\u201327). Open-vocabulary detr with conditional matching. Proceedings of the European Conference on Computer Vision (ECCV), Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-20077-9_7"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cheng, T., Song, L., Ge, Y., Liu, W., Wang, X., and Shan, Y. (2024, January 16\u201322). Yolo-world: Real-time open-vocabulary object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01599"},{"key":"ref_32","unstructured":"Liu, S., Zeng, Z., Ren, T., Li, F., Zhang, H., Yang, J., Jiang, Q., Li, C., Yang, J., and Su, H. (October, January 29). Grounding dino: Marrying dino with grounded pre-training for open-set object detection. Proceedings of the European Conference on Computer Vision (ECCV), Milan, Italy."},{"key":"ref_33","unstructured":"Zhao, T., Liu, P., He, X., Zhang, L., and Lee, K. (2024). Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, L.H., Zhang, P., Zhang, H., Yang, J., Li, C., Zhong, Y., Wang, L., Yuan, L., Zhang, L., and Hwang, J.N. (2022, January 18\u201324). Grounded language-image pre-training. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01069"},{"key":"ref_35","unstructured":"Chuang, L., Yi, J., Lizhen, Q., Zehuan, Y., and Jianfei, C. (2024, January 16\u201322). Generative region-language pretraining for open-ended object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yao, L., Pi, R., Han, J., Liang, X., Xu, H., Zhang, W., Li, Z., and Xu, D. (2024, January 16\u201322). DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.02586"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Cen, J., Yun, P., Cai, J., Wang, M.Y., and Liu, M. (2021, January 1\u20133). Open-set 3D object detection. Proceedings of the 2021 International Conference on 3D Vision (3DV), Virtually.","DOI":"10.1109\/3DV53792.2021.00095"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lu, Y., Xu, C., Wei, X., Xie, X., Tomizuka, M., Keutzer, K., and Zhang, S. (2023, January 17\u201324). Open-vocabulary point-cloud object detection without 3D annotation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00121"},{"key":"ref_39","first-page":"71862","article-title":"Coda: Collaborative novel box discovery and cross-modal alignment for open-vocabulary 3D object detection","volume":"36","author":"Cao","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_40","unstructured":"Zhu, C., Zhang, W., Wang, T., Liu, X., and Chen, K. (2023). Object2scene: Putting objects in context for open-vocabulary 3D detection. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, Y., Liu, T., Zhao, H., and Wang, S. (2024). OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality Propagation. arXiv.","DOI":"10.1007\/978-3-031-72970-6_5"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xue, L., Gao, M., Xing, C., Mart\u00edn-Mart\u00edn, R., Wu, J., Xiong, C., Xu, R., Niebles, J.C., and Savarese, S. (2023, January 17\u201324). Ulip: Learning a unified representation of language, images, and point clouds for 3D understanding. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00120"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, R., Guo, Z., Zhang, W., Li, K., Miao, X., Cui, B., Qiao, Y., Gao, P., and Li, H. (2022, January 18\u201324). Pointclip: Point cloud understanding by clip. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00836"},{"key":"ref_44","unstructured":"Zhang, H., Jiang, H., Yao, Q., Sun, Y., Zhang, R., Zhao, H., Li, H., Zhu, H., and Yang, Z. (2025, January 19\u201323). Detect anything 3D in the wild. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Honolulu, HI, USA."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Zisserman, A. (2003). Multiple View Geometry in Computer Vision, Cambridge University Press.","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Deitke, M., Schwenk, D., Salvador, J., Weihs, L., Michel, O., VanderBilt, E., Schmidt, L., Ehsani, K., Kembhavi, A., and Farhadi, A. (2023, January 17\u201324). Objaverse: A universe of annotated 3D objects. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01263"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Liu, X., Xue, N., and Wu, T. (2022, January 24\u201328). Learning auxiliary monocular contexts helps monocular 3D object detection. Proceedings of the AAAI Conference on Artificial Intelligence, Pomona, CA, USA.","DOI":"10.1609\/aaai.v36i2.20074"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yan, L., Yan, P., Xiong, S., Xiang, X., and Tan, Y. (2024, January 16\u201322). Monocd: Monocular 3D object detection with complementary depths. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00976"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wu, Z., Gan, Y., Wu, Y., Wang, R., Wang, X., and Pu, J. (2024, January 26\u201327). Fd3d: Exploiting foreground depth map for feature-supervised monocular 3D object detection. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v38i6.28436"},{"key":"ref_50","unstructured":"Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2022, January 25\u201329). LoRA: Low-Rank Adaptation of Large Language Models. Proceedings of the International Conference on Learning Representations (ICLR), Virtually."},{"key":"ref_51","first-page":"978","article-title":"Thermal infrared remote sensing","volume":"10","author":"Kuenzer","year":"2013","journal-title":"Remote Sens. Digit. Image Process."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"144","DOI":"10.3103\/S8756699015020065","article-title":"Range of operation of thermal imaging systems. Part II. Algorithm, initial data, and calculation results","volume":"51","author":"Tymkul","year":"2015","journal-title":"Optoelectron. Instrum. Data Process."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3395","DOI":"10.1109\/JSEN.2023.3234335","article-title":"Recent Advances in Thermal Imaging and its Applications Using Machine Learning: A Review","volume":"23","author":"Wilson","year":"2023","journal-title":"IEEE Sens. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/8\/2380\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T09:06:37Z","timestamp":1776071197000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/8\/2380"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":53,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["s26082380"],"URL":"https:\/\/doi.org\/10.3390\/s26082380","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,13]]}}}