{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:05:00Z","timestamp":1760234700444,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T00:00:00Z","timestamp":1623715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Structure from motion (SfM) has been treated as a mature technique to carry out the task of image orientation and 3D reconstruction. However, it is an ongoing challenge to obtain correct reconstruction results from image sets consisting of problematic match pairs. This paper investigated two types of problematic match pairs, stemming from repetitive structures and very short baselines. We built a weighted view-graph based on all potential match pairs and propose a progressive SfM method (PRMP-PSfM) that iteratively prioritizes and refines its match pairs (or edges). The method has two main steps: initialization and expansion. Initialization is developed for reliable seed reconstruction. Specifically, we prioritize a subset of match pairs by the union of multiple independent minimum spanning trees and refine them by the idea of cycle consistency inference (CCI), which aims to infer incorrect edges by analyzing the geometric consistency over cycles of the view-graph. The seed reconstruction is progressively expanded by iteratively adding new minimum spanning trees and refining the corresponding match pairs, and the expansion terminates when a certain completeness of the block is achieved. Results from evaluations on several public datasets demonstrate that PRMP-PSfM can successfully accomplish the image orientation task for datasets with repetitive structures and very short baselines and can obtain better or similar accuracy of reconstruction results compared to several state-of-the-art incremental and hierarchical SfM methods.<\/jats:p>","DOI":"10.3390\/rs13122340","type":"journal-article","created":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T21:24:29Z","timestamp":1623792269000},"page":"2340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Progressive Structure from Motion by Iteratively Prioritizing and Refining Match Pairs"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0102-8526","authenticated-orcid":false,"given":"Teng","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"}]},{"given":"Qingsong","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"}]},{"given":"Weile","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"}]},{"given":"Fei","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"F\u00f6rstner, W., and Wrobel, B.P. (2016). Photogrammetric Computer Vision, Springer.","DOI":"10.1007\/978-3-319-11550-4"},{"key":"ref_2","unstructured":"McGlone, C., Mikhail, E., and Bethel, J. (2004). Manual of Photogrammetry, American Society of Photogrammetry. [5th ed.]."},{"key":"ref_3","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."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1038\/293133a0","article-title":"A computer algorithm for reconstructing a scene from two projections","volume":"293","year":"1981","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.isprsjprs.2006.03.005","article-title":"Recent developments on direct relative orientation","volume":"60","author":"Stewenius","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sweeney, C., Sattler, T., Hollerer, T., Turk, M., and Pollefeys, M. (2015, January 7\u201313). Optimizing the viewing graph for structure-from-motion. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.98"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shen, T., Zhu, S., Fang, T., Zhang, R., and Quan, L. (2016). Graph-based consistent matching for structure-from-motion. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46487-9_9"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107712","DOI":"10.1016\/j.patcog.2020.107712","article-title":"View-graph construction framework for robust and efficient structure-from-motion","volume":"114","author":"Cui","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.isprsjprs.2021.02.002","article-title":"A hybrid global structure from motion method for synchronously estimating global rotations and global translations","volume":"174","author":"Wang","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","unstructured":"Triggs, B., McLauchlan, P.F., Hartley, R.I., and Fitzgibbon, A.W. (1999). Bundle adjustment\u2014A modern synthesis. International Workshop on Vision Algorithms, Springer.","DOI":"10.1007\/3-540-44480-7_21"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wu, C. (July, January 29). Towards linear-time incremental structure from motion. Proceedings of the 2013 International Conference on 3D Vision-3DV 2013, Seattle, WA, USA.","DOI":"10.1109\/3DV.2013.25"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mayer, H. (2014). Efficient hierarchical triplet merging for camera pose estimation. German Conference on Pattern Recognition, Springer.","DOI":"10.1007\/978-3-319-11752-2_32"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Xie, X., Yang, T., Li, D., Li, Z., and Zhang, Y. (2019). Hierarchical clustering-aligning framework based fast large-scale 3D reconstruction using aerial imagery. Remote Sens., 11.","DOI":"10.3390\/rs11030315"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Govindu, V.M. (2006). Robustness in motion averaging. Asian Conference on Computer Vision, Springer.","DOI":"10.1007\/11612704_46"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wilson, K., and Snavely, N. (2014). Robust global translations with 1dsfm. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10578-9_5"},{"key":"ref_22","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_23","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 kd forests and global pose estimation","volume":"147","author":"Wang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.isprsjprs.2020.04.016","article-title":"Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools","volume":"167","author":"Jiang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Wang, X., Xiao, T., Gruber, M., and Heipke, C. (2019, January 16\u201317). Robustifying relative orientations with respect to repetitive structures and very short baselines for global SfM. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00349"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Enqvist, O., Kahl, F., and Olsson, C. (2011, January 6\u201313). Non-sequential structure from motion. Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130252"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"299","DOI":"10.14358\/PERS.86.5.299","article-title":"An Improved Method of Refining Relative Orientation in Global Structure from Motion with a Focus on Repetitive Structure and Very Short Baselines","volume":"86","author":"Wang","year":"2020","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.isprsjprs.2020.05.020","article-title":"Structure from motion for complex image sets","volume":"166","author":"Michelini","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","unstructured":"Jiang, N., Tan, P., and Cheong, L.F. (2012, January 16\u201321). Seeing double without confusion: Structure-from-motion in highly ambiguous scenes. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Heinly, J., Dunn, E., and Frahm, J.M. (2014). Correcting for duplicate scene structure in sparse 3D reconstruction. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10593-2_51"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zach, C., Klopschitz, M., and Pollefeys, M. (2010, January 13\u201318). Disambiguating visual relations using loop constraints. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539801"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Moulon, P., Monasse, P., Perrot, R., and Marlet, R. (2016). Openmvg: Open multiple view geometry. International Workshop on Reproducible Research in Pattern Recognition, Springer.","DOI":"10.1007\/978-3-319-56414-2_5"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jiang, N., Cui, Z., and Tan, P. (2013, January 1\u20138). A global linear method for camera pose registration. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.66"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cui, H., Shen, S., Gao, W., and Wang, Z. (2018, January 5\u20138). Progressive large-scale structure-from-motion with orthogonal msts. Proceedings of the 2018 International Conference on 3D Vision (3DV), Verona, Italy.","DOI":"10.1109\/3DV.2018.00020"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Snavely, N., Seitz, S.M., and Szeliski, R. (2008, January 24\u201326). Skeletal graphs for efficient structure from motion. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587678"},{"key":"ref_37","first-page":"61","article-title":"Linear Global Translation Estimation with Feature Tracks","volume":"3","author":"Cui","year":"2014","journal-title":"Proc. ECCV"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/18.910572","article-title":"Factor graphs and the sum-product algorithm","volume":"47","author":"Kschischang","year":"2001","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1002\/j.1538-7305.1957.tb01515.x","article-title":"Shortest Connection Networks and Some Generalizations","volume":"36","author":"Prim","year":"1957","journal-title":"Bell Syst. Tech. J."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cheng, J., Leng, C., Wu, J., Cui, H., and Lu, H. (2014, January 23\u201328). Fast and accurate image matching with cascade hashing for 3D reconstruction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.8"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Cohen, A., Zach, C., Sinha, S.N., and Pollefeys, M. (2012, January 16\u201321). Discovering and exploiting 3D symmetries in structure from motion. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6247841"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2340\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:14:24Z","timestamp":1760163264000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2340"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,15]]},"references-count":41,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13122340"],"URL":"https:\/\/doi.org\/10.3390\/rs13122340","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,6,15]]}}}