{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T17:05:23Z","timestamp":1774890323841,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,29]],"date-time":"2023-01-29T00:00:00Z","timestamp":1674950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Basal Research Fund of CASM","award":["AR2215"],"award-info":[{"award-number":["AR2215"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The structure from motion (SfM) method has achieved great success in 3D sparse reconstruction, but it still faces serious challenges in large-scale scenes. Existing hybrid SfM methods usually do not fully consider the compactness between images and the connectivity between subclusters, resulting in a loose spatial distribution of images within subclusters, unbalanced connectivity between subclusters, and poor robustness in the merging stage. In this paper, an efficient and robust hybrid SfM method is proposed. First, the multifactor joint scene partition measure and the preassignment balanced image expansion algorithm among subclusters are constructed, which effectively solves the loose spatial distribution of images in subclusters problem and improves the degree of connection among subclusters. Second, the global GlobalACSfM method is used to complete the local sparse reconstruction of the subclusters under the cluster parallel framework. Then, a decentralized dynamic merging rule considering the connectivity of subclusters is proposed to realize robust merging among subclusters. Finally, public datasets and oblique photography datasets are used for experimental verification. The results show that the method proposed in this paper is superior to the state-of-the-art methods in terms of accuracy and robustness and has good feasibility and advancement prospects.<\/jats:p>","DOI":"10.3390\/rs15030769","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T10:19:28Z","timestamp":1675073968000},"page":"769","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An Efficient and Robust Hybrid SfM Method for Large-Scale Scenes"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3461-7685","authenticated-orcid":false,"given":"Zhendong","family":"Liu","sequence":"first","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"Chinese Academy of Surveying & Mapping, Beijing 100036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhu","family":"Qv","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying & Mapping, Beijing 100036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haolin","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongliang","family":"Guan","sequence":"additional","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuaizhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s11263-007-0107-3","article-title":"Modeling the World from Internet Photo Collections","volume":"80","author":"Snavely","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1145\/2001269.2001293","article-title":"Building Rome in a Day","volume":"54","author":"Agarwal","year":"2011","journal-title":"Commun. ACM"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Carrivick, J.L., Smith, M.W., and Quincey, D.J. (2016). Structure from Motion in the Geosciences, John Wiley & Sons.","DOI":"10.1002\/9781118895818"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s11042-010-0660-6","article-title":"Augmented Reality Technologies, Systems and Applications","volume":"51","author":"Carmigniani","year":"2011","journal-title":"Multimed. Tools. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.autcon.2012.09.017","article-title":"Image-Based 3D Scene Reconstruction and Exploration in Augmented Reality","volume":"33","author":"Yang","year":"2013","journal-title":"Autom. Constr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1177\/0309133319837454","article-title":"Low-Budget Topographic Surveying Comes of Age: Structure from Motion Photogrammetry in Geography and the Geosciences","volume":"43","author":"Anderson","year":"2019","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1177\/0309133315615805","article-title":"Structure from Motion Photogrammetry in Physical Geography","volume":"40","author":"Smith","year":"2016","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.jas.2014.02.030","article-title":"A Comparative Assessment of Structure from Motion Methods for Archaeological Research","volume":"46","author":"Green","year":"2014","journal-title":"J. Archaeol. Sci."},{"key":"ref_9","first-page":"495","article-title":"3D Modelling in Archaeology: The Application of Structure from Motion Methods to the Study of the Megalithic Necropolis of Panoria (Granada, Spain)","volume":"10","author":"Romero","year":"2016","journal-title":"J. Archaeol. Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ferrer, G., Garrell, A., and Sanfeliu, A. (2013, January 25\u201327). Social-Aware Robot Navigation in Urban Environments. Proceedings of the 2013 European Conference on Mobile Robots, Barcelona, Spain.","DOI":"10.1109\/ECMR.2013.6698863"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2559","DOI":"10.1109\/TSMC.2017.2745419","article-title":"Structure From Motion Technique for Scene Detection Using Autonomous Drone Navigation","volume":"49","author":"Huang","year":"2019","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Havlena, M., Torii, A., and Pajdla, T. (2010, January 5\u201311). Efficient Structure from Motion by Graph Optimization. Proceedings of the European Conference on Computer Vision, Heraklion, Greece.","DOI":"10.1007\/978-3-642-15552-9_8"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kneip, L., Scaramuzza, D., and Siegwart, R. (2011, January 20\u201325). A Novel Parametrization of the Perspective-Three-Point Problem for a Direct Computation of Absolute Camera Position and Orientation. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995464"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sweeney, C., Hollerer, T., and Turk, M. (2015, January 26\u201330). Theia: A Fast and Scalable Structure-from-Motion Library. Proceedings of the 23rd ACM International Conference on Multimedia, Ottawa, ON, Canada.","DOI":"10.1145\/2733373.2807405"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.isprsjprs.2019.08.005","article-title":"Efficient and Robust Large-Scale Structure-from-Motion via Track Selection and Camera Prioritization","volume":"156","author":"Cui","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","first-page":"II","article-title":"Combining Two-View Constraints for Motion Estimation","volume":"Volume 2","author":"Govindu","year":"2001","journal-title":"Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Crandall, D., Owens, A., Snavely, N., and Huttenlocher, D. (2011, January 20\u201325). Discrete-Continuous Optimization for Large-Scale Structure from Motion. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995626"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cui, Z., and Tan, P. (2015, January 13\u201316). Global Structure-from-Motion by Similarity Averaging. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.105"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.isprsjprs.2018.11.009","article-title":"Structure from Motion for Ordered and Unordered Image Sets Based on Random K-d Forests and Global Pose Estimation","volume":"147","author":"Wang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107537","DOI":"10.1016\/j.patcog.2020.107537","article-title":"Graph-Based Parallel Large Scale Structure from Motion","volume":"107","author":"Chen","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhu, S., Zhang, R., Zhou, L., Shen, T., Fang, T., Tan, P., and Quan, L. (2018, January 18\u201323). Very Large-Scale Global Sfm by Distributed Motion Averaging. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00480"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Farenzena, M., Fusiello, A., and Gherardi, R. (October, January 27). Structure-and-Motion Pipeline on a Hierarchical Cluster Tree. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, Kyoto, Japan.","DOI":"10.1109\/ICCVW.2009.5457435"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"95","DOI":"10.5194\/isprs-annals-V-2-2020-95-2020","article-title":"A Hybrid Global Image Orientation Method for Simultaneously Estimating Global Rotations and Global Translations","volume":"5","author":"Wang","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1109\/TPAMI.2004.85","article-title":"Drift Detection and Removal for Sequential Structure from Motion Algorithms","volume":"26","author":"Cornelis","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cui, H., Gao, X., Shen, S., and Hu, Z. (2017, January 21\u201326). HSfM: Hybrid Structure-from-Motion. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.257"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gherardi, R., Farenzena, M., and Fusiello, A. (2010, January 13\u201318). Improving the Efficiency of Hierarchical Structure-and-Motion. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539782"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.cviu.2015.05.011","article-title":"Hierarchical Structure-and-Motion Recovery from Uncalibrated Images","volume":"140","author":"Toldo","year":"2015","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ni, K., and Dellaert, F. (2012, January 13\u201315). HyperSfM. Proceedings of the Visualization & Transmission 2012 Second International Conference on 3D Imaging, Modeling, Processing, Zurich, Switzerland.","DOI":"10.1109\/3DIMPVT.2012.47"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.isprsjprs.2018.04.007","article-title":"Linear SFM: A Hierarchical Approach to Solving Structure-from-Motion Problems by Decoupling the Linear and Nonlinear Components","volume":"141","author":"Zhao","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.isprsjprs.2021.09.019","article-title":"Robust Hierarchical Structure from Motion for Large-Scale Unstructured Image Sets","volume":"181","author":"Xu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cremers, D., Reid, I., Saito, H., and Yang, M.-H. (2015). Proceedings of the Computer Vision\u2014ACCV 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-16814-2"},{"key":"ref_32","unstructured":"Zhu, S., Shen, T., Zhou, L., Zhang, R., Wang, J., Fang, T., and Quan, L. (2017). Parallel Structure from Motion from Local Increment to Global Averaging. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"114400","DOI":"10.1109\/ACCESS.2019.2923667","article-title":"Block Partitioning and Merging for Processing Large-Scale Structure From Motion Problems in Distributed Manner","volume":"7","author":"Lu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5413013","DOI":"10.1109\/TGRS.2022.3222776","article-title":"Parallel Structure From Motion for UAV Images via Weighted Connected Dominating Set","volume":"60","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","unstructured":"Moulon, P., Monasse, P., and Marlet, R. (2003, January 13\u201316). Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion. Proceedings of the IEEE International Conference on Computer Vision, Nice, France."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sweeney, C., Fragoso, V., H\u00f6llerer, T., and Turk, M. (2016, January 25\u201328). Large Scale SfM with the Distributed Camera Model. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.31"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized Cuts and Image Segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","unstructured":"(2022, June 23). Ceres Solver. Available online: http:\/\/www.ceres-solver.org\/."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., and Frahm, J.-M. (2016, January 27\u201330). Structure-From-Motion Revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kerautret, B., Colom, M., and Monasse, P. (2017). Proceedings of the Reproducible Research in Pattern Recognition, Springer International Publishing.","DOI":"10.1007\/978-3-319-56414-2"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/769\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:18:58Z","timestamp":1760120338000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/769"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,29]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15030769"],"URL":"https:\/\/doi.org\/10.3390\/rs15030769","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,29]]}}}