{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T11:06:16Z","timestamp":1769598376805,"version":"3.49.0"},"reference-count":72,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T00:00:00Z","timestamp":1623024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MF132"],"award-info":[{"award-number":["ZR2020MF132"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072020"],"award-info":[{"award-number":["62072020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China","award":["2017YFB1002602"],"award-info":[{"award-number":["2017YFB1002602"]}]},{"name":"Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University","award":["No.VRLAB2019A03"],"award-info":[{"award-number":["No.VRLAB2019A03"]}]},{"name":"Qingdao Leading Scholars Project on Innovation and Entrepreneurship 2019","award":["No.19-3-2-21-zhc"],"award-info":[{"award-number":["No.19-3-2-21-zhc"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Scene reconstruction uses images or videos as input to reconstruct a 3D model of a real scene and has important applications in smart cities, surveying and mapping, military, and other fields. Structure from motion (SFM) is a key step in scene reconstruction, which recovers sparse point clouds from image sequences. However, large-scale scenes cannot be reconstructed using a single compute node. Image matching and geometric filtering take up a lot of time in the traditional SFM problem. In this paper, we propose a novel divide-and-conquer framework to solve the distributed SFM problem. First, we use the global navigation satellite system (GNSS) information from images to calculate the GNSS neighborhood. The number of images matched is greatly reduced by matching each image to only valid GNSS neighbors. This way, a robust matching relationship can be obtained. Second, the calculated matching relationship is used as the initial camera graph, which is divided into multiple subgraphs by the clustering algorithm. The local SFM is executed on several computing nodes to register the local cameras. Finally, all of the local camera poses are integrated and optimized to complete the global camera registration. Experiments show that our system can accurately and efficiently solve the structure from motion problem in large-scale scenes.<\/jats:p>","DOI":"10.3390\/s21113939","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T22:23:00Z","timestamp":1623104580000},"page":"3939","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Parallel Structure from Motion for Sparse Point Cloud Generation in Large-Scale Scenes"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1010-7229","authenticated-orcid":false,"given":"Yongtang","family":"Bao","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality and Technology, Beihang University, Beijing 100191, China"},{"name":"Virtual Reality Research Institute, Beihang University Qingdao Research Institute, Qingdao 266100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality and Technology, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Qi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality and Technology, Beihang University, Beijing 100191, China"},{"name":"Virtual Reality Research Institute, Beihang University Qingdao Research Institute, Qingdao 266100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8140-1882","authenticated-orcid":false,"given":"Zhihui","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxiang","family":"Du","sequence":"additional","affiliation":[{"name":"Virtual Reality Research Institute, Beihang University Qingdao Research Institute, Qingdao 266100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Fan","sequence":"additional","affiliation":[{"name":"MiningLamp Technology, Beijing 100102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,7]]},"reference":[{"key":"ref_1","unstructured":"Li, Y., Tsin, Y., Genc, Y., and Kanade, T. (2005, January 20\u201326). Object detection using 2d spatial ordering constraints. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1109\/TGRS.2011.2160645","article-title":"A simple and robust feature point matching algorithm based on restricted spatial order constraints for aerial image registration","volume":"50","author":"Liu","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2019.04.006","article-title":"Reliable image matching via photometric and geometric constraints structured by delaunay triangulation","volume":"153","author":"Jiang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Aliakbarpour, H., Palaniappan, K., and Seetharaman, G. (2015, January 7\u201313). Fast structure from motion for sequential and wide area motion imagery. Proceedings of the IEEE International Conference on Computer Vision Workshop (ICCVW), Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.142"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Frahm, J., Fite-Georgel, P., Gallup, D., Johnson, T., Raguram, R., Wu, C., Jen, Y.H., Dunn, E., Clipp, B., and Lazebnik, S. (2010, January 5\u201311). Building rome on a cloudless day. Proceedings of the European Conference on Computer Vision (ECCV), Crete, Greece.","DOI":"10.1007\/978-3-642-15561-1_27"},{"key":"ref_7","unstructured":"Jiang, N., Tan, T., and Cheong, L. (2012, January 16\u201321). Seeing double without confusion: Structure-from-motion in highly ambiguous scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1023\/B:VISI.0000025798.50602.3a","article-title":"Visual modeling with a hand-held camera","volume":"59","author":"Pollefeys","year":"2004","journal-title":"Int. J. Comput. Vision"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Schonberger, J., and Frahm, J. (2016, January 27\u201330). Structure-from-motion revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1145\/1141911.1141964","article-title":"Photo tourism: Exploring image collections in 3D","volume":"25","author":"Snavely","year":"2006","journal-title":"ACM Trans. Graph."},{"key":"ref_11","unstructured":"Wu, C. (July, January 29). Towards linear-time incremental structure from motion. Proceedings of the International Conference on 3D Vision (3DV), Seattle, WA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Eriksson, A., Bastian, J., Chin, T., and Isaksson, M. (2016, January 27\u201330). A consensus-based framework for distributed bundle adjustment. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.194"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ni, K., Steedly, D., and Dellaert, F. (2007, January 14\u201320). Out-of-core bundle adjustment for large-scale 3d reconstruction. Proceedings of the International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4409085"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Triggs, B., McLauchlan, P., Hartley, R., and Fitzgibbon, A. (1999, January 21\u201322). Bundle adjustment\u2014A modern synthesis. Proceedings of the International Workshop on Vision Algorithms: Theory and Practice, Corfu, Greece.","DOI":"10.1007\/3-540-44480-7_21"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Arie-Nachimson, M., Kovalsky, S., KemelmacherShlizerman, I., Singer, A., and Basri, R. (2012, January 13\u201315). Global motion estimation from point matches. Proceedings of the 3DIMPVT, Zurich, Switzerland.","DOI":"10.1109\/3DIMPVT.2012.46"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Brand, M., Antone, M., and Teller, S. (2004, January 11\u201314). Spectral solution of large-scale extrinsic camera calibration as a graph embedding problem. Proceedings of the European Conference on Computer Vision (ECCV), Prague, Czech Republic.","DOI":"10.1007\/978-3-540-24671-8_21"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Carlone, L., Tron, R., Daniilidis, K., and Dellaert, F. (2015, January 26\u201330). Initialization techniques for 3D slam: A survey on rotation estimation and its use in pose graph optimization. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139836"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chatterjee, A., and Govindu, V. (2013, January 1\u20138). Efficient and robust largescale rotation averaging. Proceedings of the International Conference on Computer Vision (ICCV), Sydney, Australia.","DOI":"10.1109\/ICCV.2013.70"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cui, Z., Jiang, N., Tang, C., and Tan, P. (2015, January 7\u201310). Linear global translation estimation with feature tracks. Proceedings of the British Machine Vision Conference (BMVC), Swansea, UK.","DOI":"10.5244\/C.29.46"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cui, Z., and Tan, P. (2015, January 7\u201313). Global structure-from-motion by similarity averaging. Proceedings of the International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.105"},{"key":"ref_21","unstructured":"Govindu, V. (2001, January 8\u201314). Combining two-view constraints for motion estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, HI, USA."},{"key":"ref_22","unstructured":"Govindu, V. (July, January 27). Lie-algebraic averaging for globally consistent motion estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Washington, DC, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Haner, S., and Heyden, A. (2012, January 7\u201313). Covariance propagation and next best view planning for 3d reconstruction. Proceedings of the Annual Swedish Symposium on Image Analysis (SSBA), In Proceedings of the European Conference on Computer Vision (ECCV), Firenze, Italy.","DOI":"10.1007\/978-3-642-33709-3_39"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s11263-012-0601-0","article-title":"Rotation averaging","volume":"68","author":"Hartley","year":"2013","journal-title":"Int. J. Comput. Vision"},{"key":"ref_25","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995464"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ozyesil, O., and Singer, A. (2015, January 7\u201312). Robust camera location estimation by convex programming. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298883"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sweeney, C., Fragoso, V., Hollerer, T., and Turk, M. (2016, January 25\u201328). Large-scale SFM with the distributed camera model. Proceedings of the International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.31"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, X., Wu, C., Zach, C., Lazebnik, S., and Frahm, J. (2008, January 12\u201318). Modeling and recognition of landmark image collections using iconic scene graphs. Proceedings of the European Conference on Computer Vision (ECCV), Marseille, France.","DOI":"10.1007\/978-3-540-88682-2_33"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhu, S., Shen, T., Wang, J., Fang, T., and Quan, L. (2017, January 22\u201329). Progressive large scale-invariant image matching in scale space. Proceedings of the International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.259"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shen, T., Zhu, S., Fang, T., Zhang, R., and Quan, L. (2016, January 11\u201314). Graphbased consistent matching for structure-from-motion. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9_9"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Moulon, P., Monasse, P., and Marlet, R. (2013, January 1\u20138). Global fusion of relative motions for robust, accurate and scalable structure from motion. Proceedings of the International Conference on Computer Vision (ICCV), Sydney, Australia.","DOI":"10.1109\/ICCV.2013.403"},{"key":"ref_32","unstructured":"Shen, T., Wang, J., Fang, T., Zhu, S., and Quan, L. (2016, January 20\u201324). Color correction for image-based modeling in the large. Proceedings of the Asian Conference on Computer Vision (ACCV), Taipei, Taiwan."},{"key":"ref_33","unstructured":"Sinha, S., Steedly, D., and Szeliski, R. (2010, January 10\u201311). A multi-stage linear approach to structure from motion. Proceedings of the European Conference on Computer Vision workshop RMLE(ECCV), Crete, Greece."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Martinec, D., and Pajdla, T. (2007, January 17\u201322). Robust rotation and translation estimation in multiview reconstruction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383115"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/TPAMI.2004.17","article-title":"An efficient solution to the five-point relative pose problem","volume":"26","author":"Nister","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","unstructured":"Sim, K., and Hartley, R. (2006, January 17\u201322). Recovering camera motion using minimization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New York, NY, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wilson, K., Bindel, D., and Snavely, N. (2016, January 11\u201314). When is rotations averaging hard?. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46478-7_16"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yao, Y., Li, S., Zhu, S., Fang, T., Deng, H., and Quan, L. (2017, January 10\u201312). Relative camera refinement for accurate dense reconstruction. Proceedings of the International Conference on 3D Vision (3DV), Qingdao, China.","DOI":"10.1109\/3DV.2017.00030"},{"key":"ref_39","unstructured":"Farenzena, M., Fusiello, A., and Gherardi, R. (October, January 27). Structure and motion pipeline on a hierarchical cluster tree. Proceedings of the International Conference on Computer Vision Workshops (ICCV Workshops), Kyoto, Japan."},{"key":"ref_40","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 (ECCV), Crete, Greece.","DOI":"10.1007\/978-3-642-15552-9_8"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1109\/TPAMI.2005.44","article-title":"A quasi-dense approach to surface reconstruction from uncalibrated images","volume":"27","author":"Lhuillier","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Resch, B., Lensch, H., Wang, O., Pollefeys, M., and Hornung, A. (2015, January 7\u201312). Scalable structure from motion for densely sampled videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299019"},{"key":"ref_43","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_44","unstructured":"Zhu, S., Shen, T., Zhou, L., Zhang, R., Wang, J., Fang, T., and Quan, L. (2017, January 21\u201326). Parallel structure from motion from local increment to global averaging. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhu, S., Zhang, R., Zhou, L., Shen, T., Fang, T., Tan, P., and Quan, L. (2018, January 18\u201322). Very large-scale global SFM by distributed motion averaging. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00480"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bhowmick, B., Patra, S., Chatterjee, A., Govindu, V., and Banerjee, S. (2014, January 1\u20135). Divide and conquer: Efficient large-scale structure from motion using graph partitioning. Proceedings of the Asian Conference on Computer Vision (ACCV), Singapore.","DOI":"10.1007\/978-3-319-16808-1_19"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1944","DOI":"10.1109\/TPAMI.2007.1115","article-title":"Weighted graph cuts without eigenvectors a multilevel approach","volume":"29","author":"Dhillon","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","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_49","unstructured":"Kwang, M., Eduard, T., Vincent, L., and Pascal, F. (2016, January 11\u201314). Lift: Learned invariant feature transform. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands."},{"key":"ref_50","unstructured":"Daniel, D., Tomasz, M., and Andrew, R. (2018, January 18\u201322). Superpoint: Self-supervised interest point detection and description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA."},{"key":"ref_51","unstructured":"Seo, Y., and Hartley, R. (2007, January 14\u201320). A fast method to minimize error norm for geometric vision problems. Proceedings of the International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil."},{"key":"ref_52","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2016). Instance normalization: The missing ingredient for fast stylization. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Bian, J., Lin, W., and Matsushita, Y. (2017, January 21\u201326). Gms: Grid-based motion statistics for fast, ultra-robust feature correspondence. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.302"},{"key":"ref_54","unstructured":"Zhang, J., Sun, D., and Luo, Z. (November, January 27). Learning two-view correspondences and geometry using order-aware network. Proceedings of the International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_55","unstructured":"Tabb, A., and Medeiros, H. (2019). Calibration of asynchronous camera networks for object reconstruction tasks. arXiv."},{"key":"ref_56","unstructured":"Liu, Z., Zhou, S., and Suo, C. (November, January 27). Lpd-net: 3D point cloud learning for large-scale place recognition and environment analysis. Proceedings of the International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Yao, Y., Luo, Z., and Li, S. (2018, January 8\u201314). Mvsnet: Depth inference for unstructured multi-view stereo. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01237-3_47"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Gu, X., Fan, Z., and Zhu, S. (2020, January 14\u201319). Cascade cost volume for high-resolution multi-view stereo and stereo matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00257"},{"key":"ref_59","unstructured":"Miksik, O., and Vineet, V. (2019, January 16\u201320). Live Reconstruction of Large-Scale Dynamic Outdoor Worlds. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., and Xie, L. (2020, January 14\u201319). RandLA-Net: Efficient semantic segmentation of large-scale point clouds. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Heinly, J., Schonberger, J., Dunn, E., and Frahm, J. (2015, January 7\u201312). Reconstructing the world in six days. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298949"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Wilson, K., and Snavely, N. (2014, January 6\u201312). Robust global translations with 1dSFM. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10578-9_5"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Snavely, N., Seitz, S., and Szeliski, R. (2008, January 23\u201328). Skeletal graphs for efficient structure from motion. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587678"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1109\/TVCG.2015.2461163","article-title":"Image-based building regularization using structural linear features","volume":"22","author":"Wang","year":"2016","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_65","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 International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.98"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zach, C., Irschara, A., and Bischof, H. (2008, January 23\u201328). What can missing correspondences tell us about 3d structure and motion?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587707"},{"key":"ref_67","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 International Conference on Computer Vision (ICCV), Sydney, Australia.","DOI":"10.1109\/ICCV.2013.66"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-662-03714-0_1","article-title":"WGS 84-Past, Present and Future","volume":"118","author":"Slater","year":"1998","journal-title":"Int. Assoc. Geod. Symp."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Cheng, J., Leng, C., and Wu, J. (2014, January 24\u201327). Fast and accurate image matching with cascade hashing for 3d reconstruction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.8"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Sweeney, C., Hollerer, T., and Turk, M. (2015, January 23\u201326). Theia: A fast and scalable structure-from-motion library. Proceedings of the Annual ACM International Conference on Multimedia (ICMR), Shanghai, China.","DOI":"10.1145\/2733373.2807405"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Olsson, C., and Enqvist, O. (2011, January 6\u201313). Non-sequential structure from motion. Proceedings of the International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130252"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Olsson, C., and Enqvist, O. (2011, January 11\u201314). Structure from Motion for Unordered Image Collections. Proceedings of the Scandinavian conference on Image analysis(SCIA), Ystad, Sweden.","DOI":"10.1007\/978-3-642-21227-7_49"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/11\/3939\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:11:50Z","timestamp":1760163110000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/11\/3939"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,7]]},"references-count":72,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["s21113939"],"URL":"https:\/\/doi.org\/10.3390\/s21113939","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,7]]}}}