{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T02:28:15Z","timestamp":1782268095515,"version":"3.54.5"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"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":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s44443-025-00251-8","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T14:55:01Z","timestamp":1758725701000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DGGR-Net: single-image 3D reconstruction from complex backgrounds via graph-based refinement and difference-guided fusion"],"prefix":"10.1007","volume":"37","author":[{"given":"Yang","family":"Ding","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huamin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linxuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"issue":"8","key":"251_CR1","doi-asserted-by":"publisher","first-page":"1670","DOI":"10.1109\/TPAMI.2014.2377712","volume":"37","author":"JT Barron","year":"2014","unstructured":"Barron JT, Malik J (2014) Shape, illumination, and reflectance from shading. IEEE Trans Pattern Anal Mach Intell 37(8):1670\u20131687","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"251_CR2","unstructured":"Chang AX, Funkhouser T, Guibas L, Hanrahan P, Huang Q, Li Z, Savarese S, Savva M, Song S, Su H et al (2015) Shapenet: an information-rich 3d model repository. arXiv:1512.03012"},{"key":"251_CR3","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.procs.2022.10.102","volume":"209","author":"X Chen","year":"2022","unstructured":"Chen X, Liu D, Luo J, Chen T, Zhang G, Rong X, Li Y (2022) Realization of indoor and outdoor localization and navigation for quadruped robots. Proc Comput Sci 209:84\u201392","journal-title":"Proc Comput Sci"},{"key":"251_CR4","doi-asserted-by":"crossref","unstructured":"Chen C, Liu Y-S, Han Z (2024) Learning local pattern modularization for point cloud reconstruction from unseen classes. In: European conference on computer vision. Springer, pp 305\u2013323","DOI":"10.1007\/978-3-031-73195-2_18"},{"key":"251_CR5","doi-asserted-by":"crossref","unstructured":"Fan H, Su H, Guibas LJ (2017) A point set generation network for 3d object reconstruction from a single image. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 605\u2013613","DOI":"10.1109\/CVPR.2017.264"},{"issue":"1","key":"251_CR6","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1007\/s11227-021-03899-x","volume":"78","author":"AS Gezawa","year":"2022","unstructured":"Gezawa AS, Bello ZA, Wang Q, Yunqi L (2022) A voxelized point clouds representation for object classification and segmentation on 3d data. J Supercomput 78(1):1479\u20131500","journal-title":"J Supercomput"},{"issue":"5","key":"251_CR7","first-page":"926","volume":"46","author":"K H\u00e4ming","year":"2010","unstructured":"H\u00e4ming K, Peters G (2010) The structure-from-motion reconstruction pipeline-a survey with focus on short image sequences. Kybernetika 46(5):926\u2013937","journal-title":"Kybernetika"},{"key":"251_CR8","doi-asserted-by":"crossref","unstructured":"Han Z, Qiao G, Liu Y-S, Zwicker M (2020) Seqxy2seqz: structure learning for 3d shapes by sequentially predicting 1d occupancy segments from 2d coordinates. In: Computer vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXIV 16. Springer, pp 607\u2013625","DOI":"10.1007\/978-3-030-58586-0_36"},{"key":"251_CR9","unstructured":"Insafutdinov E, Dosovitskiy A (2018) Unsupervised learning of shape and pose with differentiable point clouds. Adv Neural Inf Process Syst 31"},{"key":"251_CR10","doi-asserted-by":"crossref","unstructured":"Kniaz VV, Knyaz VA, Remondino F, Bordodymov A, Moshkantsev P (2020) Image-to-voxel model translation for 3d scene reconstruction and segmentation. In: European conference on computer vision. Springer, pp 105\u2013124","DOI":"10.1007\/978-3-030-58571-6_7"},{"key":"251_CR11","doi-asserted-by":"crossref","unstructured":"Kurenkov A, Ji J, Garg A, Mehta V, Gwak J, Choy C, Savarese S (2018) Deformnet: free-form deformation network for 3d shape reconstruction from a single image. In: 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 858\u2013866","DOI":"10.1109\/WACV.2018.00099"},{"key":"251_CR12","doi-asserted-by":"crossref","unstructured":"Lee JJ, Benes B (2025) Rgb2point: 3d point cloud generation from single rgb images. In: 2025 IEEE\/CVF winter conference on applications of computer vision (WACV). IEEE, pp 2952\u20132962","DOI":"10.1109\/WACV61041.2025.00292"},{"issue":"3","key":"251_CR13","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1007\/s10489-019-01523-3","volume":"50","author":"T Ma","year":"2020","unstructured":"Ma T, Kuang P, Tian W (2020) An improved recurrent neural networks for 3d object reconstruction. Appl Intell 50(3):905\u2013923","journal-title":"Appl Intell"},{"key":"251_CR14","doi-asserted-by":"crossref","unstructured":"Mandikal P, KL N, Venkatesh\u00a0Babu R (2018) 3d-psrnet: part segmented 3d point cloud reconstruction from a single image. In: Proceedings of the European Conference on Computer Vision (ECCV) workshops, pp 0\u20130","DOI":"10.1007\/978-3-030-11015-4_50"},{"key":"251_CR15","doi-asserted-by":"crossref","unstructured":"Mandikal P, Navaneet K, Agarwal M, Venkatesh B (2018) 3d-lmnet: latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image. In: British machine vision conference 2018, BMVC 2018. BMVA Press","DOI":"10.1007\/978-3-030-11015-4_50"},{"key":"251_CR16","doi-asserted-by":"crossref","unstructured":"Melas-Kyriazi L, Rupprecht C, Vedaldi A (2023) Pc2: projection-conditioned point cloud diffusion for single-image 3d reconstruction. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12923\u201312932","DOI":"10.1109\/CVPR52729.2023.01242"},{"key":"251_CR17","doi-asserted-by":"crossref","unstructured":"Peng K, Islam R, Quarles J, Desai K (2022) Tmvnet: using transformers for multi-view voxel-based 3d reconstruction. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 222\u2013230","DOI":"10.1109\/CVPRW56347.2022.00036"},{"key":"251_CR18","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.cag.2022.01.001","volume":"102","author":"G Ping","year":"2022","unstructured":"Ping G, Esfahani MA, Chen J, Wang H (2022) Visual enhancement of single-view 3d point cloud reconstruction. Comput Graphics 102:112\u2013119","journal-title":"Comput Graphics"},{"key":"251_CR19","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/s11263-006-8323-9","volume":"71","author":"S Savarese","year":"2007","unstructured":"Savarese S, Andreetto M, Rushmeier H, Bernardini F, Perona P (2007) 3d reconstruction by shadow carving: theory and practical evaluation. Int J Comput Vision 71:305\u2013336","journal-title":"Int J Comput Vision"},{"key":"251_CR20","doi-asserted-by":"crossref","unstructured":"Shao Y, Tong G, Peng H (2022) Mining local geometric structure for large-scale 3d point clouds semantic segmentation. Neurocomputing 500:191\u2013202","DOI":"10.1016\/j.neucom.2022.05.060"},{"key":"251_CR21","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"251_CR22","doi-asserted-by":"crossref","unstructured":"Stachniss C, Leonard JJ, Thrun S (2016) Simultaneous localization and mapping. Springer Handbook of Robotics, 1153\u20131176","DOI":"10.1007\/978-3-319-32552-1_46"},{"key":"251_CR23","doi-asserted-by":"crossref","unstructured":"Sun X, Wu J, Zhang X, Zhang Z, Zhang C, Xue T, Tenenbaum JB, Freeman WT (2018) Pix3d: dataset and methods for single-image 3d shape modeling. In: IEEE conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2018.00314"},{"key":"251_CR24","doi-asserted-by":"crossref","unstructured":"Szymanowicz S, Rupprecht C, Vedaldi A (2024) Splatter image: ultra-fast single-view 3d reconstruction. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10208\u201310217","DOI":"10.1109\/CVPR52733.2024.00972"},{"issue":"18","key":"251_CR25","doi-asserted-by":"publisher","first-page":"2288","DOI":"10.3390\/math9182288","volume":"9","author":"R Tahir","year":"2021","unstructured":"Tahir R, Sargano AB, Habib Z (2021) Voxel-based 3d object reconstruction from single 2d image using variational autoencoders. Mathematics 9(18):2288","journal-title":"Mathematics"},{"key":"251_CR26","doi-asserted-by":"publisher","first-page":"108792","DOI":"10.1016\/j.patcog.2022.108792","volume":"130","author":"H Tang","year":"2022","unstructured":"Tang H, Yuan C, Li Z, Tang J (2022) Learning attention-guided pyramidal features for few-shot fine-grained recognition. Pattern Recogn 130:108792","journal-title":"Pattern Recogn"},{"key":"251_CR27","doi-asserted-by":"crossref","unstructured":"Tang H, Li Z, Zhang D, He S, Tang J (2024) Divide-and-conquer: confluent triple-flow network for rgb-t salient object detection. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2024.3511621"},{"key":"251_CR28","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neucom.2014.12.081","volume":"156","author":"Y Tian","year":"2015","unstructured":"Tian Y, Long Y, Xia D, Yao H, Zhang J (2015) Handling occlusions in augmented reality based on 3d reconstruction method. Neurocomputing 156:96\u2013104","journal-title":"Neurocomputing"},{"key":"251_CR29","doi-asserted-by":"publisher","first-page":"102438","DOI":"10.1016\/j.displa.2023.102438","volume":"78","author":"Y Tong","year":"2023","unstructured":"Tong Y, Chen H, Yang N, Menhas MI, Ahmad B (2023) 3d-cdrnet: retrieval-based dense point cloud reconstruction from a single image under complex background. Displays 78:102438","journal-title":"Displays"},{"key":"251_CR30","doi-asserted-by":"crossref","unstructured":"Tulsiani S, Efros AA, Malik J (2018) Multi-view consistency as supervisory signal for learning shape and pose prediction. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2897\u20132905","DOI":"10.1109\/CVPR.2018.00306"},{"issue":"1","key":"251_CR31","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1049\/cvi2.12136","volume":"17","author":"E Wang","year":"2023","unstructured":"Wang E, Sun H, Wang B, Cao Z, Liu Z (2023) 3d-fegnet: a feature enhanced point cloud generation network from a single image. IET Comput Vision 17(1):98\u2013110","journal-title":"IET Comput Vision"},{"key":"251_CR32","doi-asserted-by":"crossref","unstructured":"Wen X, Zhou J, Liu Y-S, Su H, Dong Z, Han Z (2022) 3d shape reconstruction from 2d images with disentangled attribute flow. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3803\u20133813","DOI":"10.1109\/CVPR52688.2022.00378"},{"key":"251_CR33","doi-asserted-by":"crossref","unstructured":"Wu Q, Ritchie D, Savva M, Chang AX (2024) Generalizing single-view 3d shape retrieval to occlusions and unseen objects. In: 2024 international conference on 3D vision (3DV). IEEE, pp 893\u2013902","DOI":"10.1109\/3DV62453.2024.00060"},{"key":"251_CR34","doi-asserted-by":"crossref","unstructured":"Xie H, Yao H, Sun X, Zhou S, Zhang S (2019) Pix2vox: context-aware 3d reconstruction from single and multi-view images. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2690\u20132698","DOI":"10.1109\/ICCV.2019.00278"},{"key":"251_CR35","doi-asserted-by":"crossref","unstructured":"Xu H, Lei Y, Chen Z, Zhang X, Zhao Y, Wang Y, Tu Z (2024) Bayesian diffusion models for 3d shape reconstruction. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10628\u201310638","DOI":"10.1109\/CVPR52733.2024.01011"},{"key":"251_CR36","doi-asserted-by":"publisher","first-page":"3746","DOI":"10.1109\/TIP.2023.3279661","volume":"32","author":"X Yang","year":"2023","unstructured":"Yang X, Lin G, Zhou L (2023) Single-view 3d mesh reconstruction for seen and unseen categories. IEEE Trans Image Process 32:3746\u20133758","journal-title":"IEEE Trans Image Process"},{"key":"251_CR37","doi-asserted-by":"crossref","unstructured":"Yang S, Xu M, Xie H, Perry S, Xia J (2021) Single-view 3d object reconstruction from shape priors in memory. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3152\u20133161","DOI":"10.1109\/CVPR46437.2021.00317"},{"key":"251_CR38","doi-asserted-by":"publisher","first-page":"105574","DOI":"10.1016\/j.knosys.2020.105574","volume":"194","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Huo K, Liu Z, Zang Y, Liu Y, Li X, Zhang Q, Wang C (2020) Pgnet: a part-based generative network for 3d object reconstruction. Knowl-Based Syst 194:105574","journal-title":"Knowl-Based Syst"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00251-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00251-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00251-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T15:35:58Z","timestamp":1761752158000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00251-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,24]]},"references-count":38,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["251"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00251-8","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,24]]},"assertion":[{"value":"2 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2025","order":3,"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":"Competing interest"}}],"article-number":"222"}}