{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T18:36:57Z","timestamp":1776710217098,"version":"3.51.2"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s00371-024-03754-z","type":"journal-article","created":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T07:33:21Z","timestamp":1735025601000},"page":"5815-5833","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing 3D Gaussian splatting for low-quality images: semantically guided training and unsupervised quality assessment"],"prefix":"10.1007","volume":"41","author":[{"given":"Zehao","family":"Cao","sequence":"first","affiliation":[]},{"given":"Zongji","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yuanben","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Cheng","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Weinan","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Zhihong","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Junyi","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,24]]},"reference":[{"issue":"1","key":"3754_CR1","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., et al.: Nerf: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99\u2013106 (2021)","journal-title":"Commun. ACM"},{"key":"3754_CR2","doi-asserted-by":"crossref","unstructured":"Barron, J.T., Mildenhall, B., Tancik, M., et al.: Mip-Nerf: a multiscale representation for anti-aliasing neural radiance fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 5855\u20135864 (2021)","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"3754_CR3","doi-asserted-by":"crossref","unstructured":"Cao, J., Wang, H., Chemerys, P., et al.: Real-time neural light field on mobile devices. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 8328\u20138337 (2023)","DOI":"10.1109\/CVPR52729.2023.00805"},{"key":"3754_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Z., Funkhouser, T., Hedman, P., et al.: MobileNerf: exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 16569\u201316578 (2023)","DOI":"10.1109\/CVPR52729.2023.01590"},{"key":"3754_CR5","doi-asserted-by":"crossref","unstructured":"Wang, Y., Han, Q., Habermann, M., et al.: Neus2: fast learning of neural implicit surfaces for multi-view reconstruction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 3295\u20133306 (2023)","DOI":"10.1109\/ICCV51070.2023.00305"},{"key":"3754_CR6","doi-asserted-by":"publisher","first-page":"70969","DOI":"10.1109\/ACCESS.2020.2987177","volume":"8","author":"A Karambakhsh","year":"2020","unstructured":"Karambakhsh, A., Sheng, B., Li, P., et al.: VoxRec: hybrid convolutional neural network for active 3D object recognition. IEEE access 8, 70969\u201370980 (2020)","journal-title":"IEEE access"},{"issue":"1","key":"3754_CR7","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TNNLS.2022.3175775","volume":"35","author":"A Karambakhsh","year":"2022","unstructured":"Karambakhsh, A., Sheng, B., Li, P., et al.: SparseVoxNet: 3-D object recognition with sparsely aggregation of 3-D dense blocks. IEEE Trans. Neural Netw. Learn. Syst. 35(1), 532\u2013546 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"3754_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3592433","volume":"42","author":"B Kerbl","year":"2023","unstructured":"Kerbl, B., Kopanas, G., Leimk\u00fchler, T., et al.: 3D Gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. 42(4), 1\u201314 (2023)","journal-title":"ACM Trans. Graph."},{"key":"3754_CR9","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Yu, C., Xie, T., et al.: VR-GS: a physical dynamics-aware interactive Gaussian splatting system in virtual reality. In: ACM SIGGRAPH 2024 Conference Papers. pp. 1\u20131 (2024)","DOI":"10.1145\/3641519.3657448"},{"key":"3754_CR10","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Li, X., Huang, Y., et al.: Gavatar: animatable 3D Gaussian avatars with implicit mesh learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 896\u2013905 (2024)","DOI":"10.1109\/CVPR52733.2024.00091"},{"key":"3754_CR11","unstructured":"Liu, Y., Li, C., Yang, C., et al.: EndoGaussian: Gaussian splatting for deformable surgical scene reconstruction. arXiv preprint arXiv:2401.12561 (2024)"},{"key":"3754_CR12","doi-asserted-by":"crossref","unstructured":"Ye, M., Danelljan, M., Yu, F., et al.: Gaussian grouping: segment and edit anything in 3D scenes. arXiv preprint arXiv:2312.00732 (2023)","DOI":"10.1007\/978-3-031-73397-0_10"},{"key":"3754_CR13","doi-asserted-by":"crossref","unstructured":"Luiten, J., Kopanas, G., Leibe, B., et al.: Dynamic 3D Gaussians: tracking by persistent dynamic view synthesis. arXiv preprint arXiv:2308.09713 (2023)","DOI":"10.1109\/3DV62453.2024.00044"},{"key":"3754_CR14","doi-asserted-by":"crossref","unstructured":"Yang, Z., Gao, X., Zhou, W., et al.: Deformable 3D Gaussians for high-fidelity monocular dynamic scene reconstruction. arXiv preprint arXiv:2309.13101 (2023)","DOI":"10.1109\/CVPR52733.2024.01922"},{"key":"3754_CR15","unstructured":"Yang, Z., Yang, H., Pan, Z., et al.: Real-time photorealistic dynamic scene representation and rendering with 4D Gaussian splatting. arXiv preprint arXiv:2310.10642 (2023)"},{"key":"3754_CR16","doi-asserted-by":"crossref","unstructured":"Wu, G., Yi, T., Fang, J., et al.: 4D Gaussian splatting for real-time dynamic scene rendering. arXiv preprint arXiv:2310.08528 (2023)","DOI":"10.1109\/CVPR52733.2024.01920"},{"key":"3754_CR17","doi-asserted-by":"crossref","unstructured":"Tancik, M., Casser, V., Yan, X., et al.: Block-Nerf: scalable large scene neural view synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 8248\u20138258 (2022)","DOI":"10.1109\/CVPR52688.2022.00807"},{"key":"3754_CR18","unstructured":"Zhenxing, M.I., Xu, D: Switch-Nerf: learning scene decomposition with mixture of experts for large-scale neural radiance fields. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"3754_CR19","unstructured":"Ramamoorthi, R: Nerfs: the search for the best 3D representation. arXiv preprint arXiv:2308.02751 (2023)"},{"key":"3754_CR20","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wang, F., Liu, H: Text-to-3D using Gaussian splatting. arXiv preprint arXiv:2309.16585 (2023)","DOI":"10.1109\/CVPR52733.2024.02022"},{"key":"3754_CR21","unstructured":"Tang, J., Ren, J., Zhou, H., et al.: DreamGaussian: generative Gaussian splatting for efficient 3D content creation. arXiv preprint arXiv:2309.16653 (2023)"},{"key":"3754_CR22","unstructured":"Yi, T., Fang, J., Wu, G., et al: GaussianDreamer: fast generation from text to 3D Gaussian splatting with point cloud priors. arXiv preprint arXiv:2310.08529 (2023)"},{"key":"3754_CR23","doi-asserted-by":"crossref","unstructured":"Zhi, S., Laidlow, T., Leutenegger, S., et al.: In-place scene labelling and understanding with implicit scene representation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 15838\u201315847 (2021)","DOI":"10.1109\/ICCV48922.2021.01554"},{"key":"3754_CR24","doi-asserted-by":"crossref","unstructured":"Fu, X., Zhang, S., Chen, T., et al.: Panoptic Nerf: 3D-to-2D label transfer for panoptic urban scene segmentation. In: 2022 International Conference on 3D Vision (3DV). IEEE. pp. 1\u201311 (2022)","DOI":"10.1109\/3DV57658.2022.00042"},{"key":"3754_CR25","doi-asserted-by":"crossref","unstructured":"Kundu, A., Genova, K., Yin, X., et al.: Panoptic neural fields: a semantic object-aware neural scene representation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 12871\u201312881 (2022)","DOI":"10.1109\/CVPR52688.2022.01253"},{"key":"3754_CR26","doi-asserted-by":"crossref","unstructured":"Siddiqui, Y., Porzi, L., Bul\u00f3, S.R., et al.: Panoptic lifting for 3D scene understanding with neural fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9043\u20139052 (2023)","DOI":"10.1109\/CVPR52729.2023.00873"},{"key":"3754_CR27","unstructured":"Wang, B., Chen, L., Yang, B: DM-Nerf: 3D scene geometry decomposition and manipulation from 2D images. arXiv preprint arXiv:2208.07227 (2022)"},{"key":"3754_CR28","first-page":"23311","volume":"35","author":"S Kobayashi","year":"2022","unstructured":"Kobayashi, S., Matsumoto, E., Sitzmann, V.: Decomposing Nerf for editing via feature field distillation. Adv. Neural. Inf. Process. Syst. 35, 23311\u201323330 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3754_CR29","doi-asserted-by":"crossref","unstructured":"Rebain, D., Jiang, W., Yazdani, S., et al.: Derf: decomposed radiance fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 14153\u201314161 (2021)","DOI":"10.1109\/CVPR46437.2021.01393"},{"key":"3754_CR30","doi-asserted-by":"crossref","unstructured":"Tschernezki, V., Laina, I., Larlus, D., et al.: Neural feature fusion fields: 3D distillation of self-supervised 2D image representations. In: 2022 International Conference on 3D Vision (3DV). IEEE. pp. 443\u2013453 (2022)","DOI":"10.1109\/3DV57658.2022.00056"},{"key":"3754_CR31","unstructured":"Vora, S., Radwan, N., Greff, K., et al.: Nesf: neural semantic fields for generalizable semantic segmentation of 3D scenes. arXiv preprint arXiv:2111.13260 (2021)"},{"key":"3754_CR32","doi-asserted-by":"crossref","unstructured":"Kerr, J., Kim, C.M., Goldberg, K., et al.: Lerf: Language embedded radiance fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 19729\u201319739 (2023)","DOI":"10.1109\/ICCV51070.2023.01807"},{"key":"3754_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-024-03632-8","author":"Y Xiang","year":"2024","unstructured":"Xiang, Y., Zhou, H., Li, C., et al.: Deep learning in motion deblurring: current status, benchmarks and future prospects. Vis. Comput. (2024). https:\/\/doi.org\/10.1007\/s00371-024-03632-8","journal-title":"Vis. Comput."},{"issue":"4","key":"3754_CR34","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1109\/TCSVT.2019.2901629","volume":"30","author":"B Sheng","year":"2019","unstructured":"Sheng, B., Li, P., Fang, X., et al.: Depth-aware motion deblurring using loopy belief propagation. IEEE Trans. Circuits Syst. Video Technol. 30(4), 955\u2013969 (2019)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3754_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-024-03611-z","author":"S Xu","year":"2024","unstructured":"Xu, S., Wang, J., He, N., et al.: Optimizing underwater image enhancement: integrating semi-supervised learning and multi-scale aggregated attention. Vis. Comput. (2024). https:\/\/doi.org\/10.1007\/s00371-024-03611-z","journal-title":"Vis. Comput."},{"key":"3754_CR36","unstructured":"Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. I\u2013I. IEEE (2003)"},{"key":"3754_CR37","doi-asserted-by":"crossref","unstructured":"Godard, C., Mac Aodha, O., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 270\u2013279 (2017)","DOI":"10.1109\/CVPR.2017.699"},{"key":"3754_CR38","unstructured":"Qi, C.R., Yi, L., Su, H., et al.: PointNet++: Deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"3754_CR39","unstructured":"Radford, A., Kim, J.W., Hallacy, C., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning. PMLR. pp. 8748\u20138763 (2021)"},{"issue":"4","key":"3754_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3528223.3530068","volume":"41","author":"Y Vinker","year":"2022","unstructured":"Vinker, Y., Pajouheshgar, E., Bo, J.Y., et al.: Clipasso: semantically-aware object sketching. ACM Trans. Graph. 41(4), 1\u201311 (2022)","journal-title":"ACM Trans. Graph."},{"key":"3754_CR41","doi-asserted-by":"crossref","unstructured":"Barron, J.T., Mildenhall, B., Verbin, D., et al.: Mip-Nerf 360: unbounded anti-aliased neural radiance fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 5470\u20135479 (2022)","DOI":"10.1109\/CVPR52688.2022.00539"},{"key":"3754_CR42","doi-asserted-by":"crossref","unstructured":"Jensen, R., Dahl, A., Vogiatzis, G., et al.: Large scale multi-view stereopsis evaluation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 406\u2013413 (2014)","DOI":"10.1109\/CVPR.2014.59"},{"key":"3754_CR43","doi-asserted-by":"crossref","unstructured":"Tancik, M., Weber, E., Ng, E., et al.: Nerfstudio: a modular framework for neural radiance field development. In: ACM SIGGRAPH 2023 Conference Proceedings. pp. 1\u201312 (2023)","DOI":"10.1145\/3588432.3591516"},{"key":"3754_CR44","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., et al.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 9650\u20139660 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03754-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03754-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03754-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T04:52:21Z","timestamp":1747371141000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03754-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,24]]},"references-count":44,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["3754"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03754-z","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5196767\/v1","asserted-by":"object"}]},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,24]]},"assertion":[{"value":"3 December 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}