Digital camera calibration method for close-up photogrammetry of large structures
Source abstract
Abstract
For large structures, the current close-up photographic calibration method has difficulty meeting the requirements of large structures that fill the camera’s field-of-view. Therefore, a digital camera calibration method for close-up photogrammetry of large structures is proposed. The size of the chequerboard calibration plate is further reduced by adopting multiple shots, and the focusing distance of the camera is set to be larger than the hyperfocal distance to realise digital camera calibration for close-up photogrammetry of large structures. Additionally, validation and application experiments are designed to complete the demonstration. The results show that the proposed method can overcome the problem that the traditional camera calibration method cannot be applied to large structures. The reprojection error after using the method is significantly lower than 0.5 pixels, and the measurement accuracy is improved from the decimetre level to the millimetre level. It is shown that the method can be applied to the close-up photogrammetry of large structures such as hillsides and dams.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: 3D Surveying and Cultural Heritage · Optical measurement and interference techniques · Remote Sensing and LiDAR Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Chongqing Jiaotong University
Chongqing University of Science and Technology
Funding
Chongqing Natural Science Foundation · Chongqing Water Conservancy Science and Technology Project · Chongqing Programme of Excellence “package” project
References (33)
Show source-provided references
1. 10.1007/978-3-030-26250-1_2810.1007/978-3-030-26250-1_28 ↗
2. 10.1016/j.engfailanal.2014.08.00410.1016/j.engfailanal.2014.08.004 ↗
3. 10.3390/app12231210310.3390/app122312103 ↗
4. Valença J, Júlio ENBS, Araújo HJ. Applications of photogrammetry to structural assessment. Exp Techniques. 2012;36(5):71–81. doi: 10/bj7nbv10.1111/j.1747-1567.2011.00731.x ↗
5. Budde LE, Schmidt J, Javanmard-Ghareshiran A, et al. Development of a database for benchmark datasets in photogrammetry and remote sensing. ISPRS Ann Photogramm Remote Sens Spat Inf Sci. 2022;V-1–2022:187–193. doi: 10/gtbr77
6. Kahmen O, Rofallski R, Conen N, et al. On scale definition within calibration of multi-camera systems in multimedia photogrammetry. Int. Arch Photogramm Remote Sens Spat Inf Sci. 2019;XLII-2/W10:93–100. doi: 10/gtbr76
7. 10.1016/j.isprsjprs.2014.02.01310.1016/j.isprsjprs.2014.02.013 ↗
8. Medeiros M Jr., Babadopulos L, Maia R, et al. 3D pavement macrotexture parameters from close range photogrammetry. Int J Pavement Eng. 2023;24(2):2020784. doi: 10/gtbr7z10.1080/10298436.2021.2020784 ↗
9. Behmann J, Mahlein A-K, Paulus S, et al. Calibration of hyperspectral close-range pushbroom cameras for plant phenotyping. ISPRS J Photogramm Remote Sens. 2015;106:172–182. doi: 10/f7k6zp10.1016/j.isprsjprs.2015.05.010 ↗
10. 10.1016/j.isprsjprs.2010.06.00310.1016/j.isprsjprs.2010.06.003 ↗
11. Jiang R, Jauregui DV. Development of a digital close-range photogrammetric bridge deflection measurement system. Measurement. 2010;43(10):1431–1438. doi: 10/c8skqn10.1016/j.measurement.2010.08.015 ↗
12. Kahn S, Bockholt U, Kuijper A, et al. Towards precise real-time 3D difference detection for industrial applications. Comput Ind. 2013;64(9):1115–1128. doi: 10/f5jmxk10.1016/j.compind.2013.04.004 ↗
13. Chen C, Yang F, Pan B. Model-free method for intrinsic camera parameters calibration and lens distortion correction. Opt Eng. 2021;60(10). doi: 10/gtbsbf10.1117/1.OE.60.10.104102 ↗
14. Santana-Cedrés D, Gomez L, Alemán-Flores M, et al. Invertibility and estimation of two-parameter polynomial and division lens distortion models. SIAM J Imag Sci. 2015;8(3):1574–1606. doi: 10/gnrdcp10.1137/151006044 ↗
15. Guan Z, Zhang G, Ge L. Correction of camera interior orientation elements based on multi-frame star map. In: IGARSS 2020 – 2020 IEEE International Geoscience and Remote Sensing Symposium; Waikoloa, (HI), USA. IEEE; 2020. p. 6238–6241. doi: 10/gtbsbp
16. 10.3788/LOP22177310.3788/LOP221773 ↗
17. 10.1631/FITEE.220007910.1631/FITEE.2200079 ↗
18. 10.16136/j.joel.2013.08.02210.16136/j.joel.2013.08.022 ↗
19. 10.1364/OL.56429410.1364/OL.564294 ↗
20. 10.1007/s10409025-25314-x10.1007/s10409025-25314-x ↗
21. 10.1007/s11263-025-02531-210.1007/s11263-025-02531-2 ↗
22. 10.1364/AO.54629410.1364/AO.546294 ↗
23. Li W, Meng H, Wang J, et al. Wide view calibration of single cameras. J Univ Sci Technol. 2007;(06):627–630.
24. Hui B. Research on non-metric camera calibration and photogrammetric motion measurement [Doctor]. National University of Defense Technology; 2013.
25. Kuai Y. Research on large-scale scene calibration method based on local objects [Master]. National University of Defense Technology; 2015.
26. 10.3969/j.issn.1671-3044.2019.04.01510.3969/j.issn.1671-3044.2019.04.015 ↗
27. Yao W, Liu T, Jiang J. Large, scale scene calibration based on single image. Comput Simul. 2020;37(11):454–459.
28. 10.1016/j.measurement.2021.10999910.1016/j.measurement.2021.109999 ↗
29. Liu W, Li X, Ma X, et al. Camera calibration method for close range large field of view camera based on compound target. Infrared Laser Eng. 2016;45:230–236.
30. Xiong X, Wang K, Chen J, et al. A calibration system of intelligent driving vehicle mounted scene projection camera based on Zhang Zhengyou calibration method. In: Teng R, Zhu X, editors. 2022 34th Chinese Control and Decision Conference CCDC; Hefei, China. IEEE; 2022. p. 747–750. doi: 10/gtbr87
31. Liang X, Du Y, Wei D. An integrated camera parameters calibration approach for robotic monocular vision guidance. In: 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation YAC; Jinzhou, China. IEEE; 2019. p. 455–459. doi: 10/gtbr8910.1109/YAC.2019.8787642 ↗
32. Ma C, Meng F, Guo Y, et al. An improved method of corner point extraction used in grating projection phase measurement. In: Liu D, Luo X, Jiang Y Lu J, editors. AOPC 2020: Optical Sensing and Imaging Technology 2020 opt. sens. imaging technol. Beijing, China: SPIE; 2020. p. 199. doi: 10/gtbsb2
33. Santana-Cedres D, Gomez L, Aleman-Flores M, et al. Estimation of the lens distortion model by minimizing a line reprojection error. IEEE Sens J. 2017;17(9):2848–2855. doi: 10/f96kv310.1109/JSEN.2017.2677475 ↗