UAV-HSfM modeling method and its empirical analysis in the complex mountainous areas of central Yunnan Plateau

doi:10.13474/j.cnki.11-2246.2025.1108

Abstract

Abstract: As a key technique within 3D reconstruction technology,the structure from motion (SfM) algorithm enables 3D scene reconstruction when integrated with UAV imagery.To address the issues of error accumulation in traditional incremental SfM(ISfM) and the high computational complexity of global SfM (GSfM) optimization in drone-based 3D reconstruction,this paper proposes a hybrid drone modeling method (UAV-HSfM). This approach first estimates camera rotations using a global algorithm and then computes camera positions through an incremental algorithm,thereby optimizing the 3D reconstruction process.The study was conducted in a mountainous test area in the central Yunnan Plateau.The results demonstrate that:①The proposed method achieves more stable feature extraction;②The sparse point cloud exhibits both completeness in quantity and geometric accuracy; ③The elevation root mean square error (RMSE) of 0.056 m shows significant improvement,with a more concentrated error distribution.In conclusion,the UAV-HSfM method effectively combines the advantages of GSfM and ISfM,significantly enhancing the accuracy of 3D reconstruction in complex mountainous terrains.

Key words: 3D reconstruction, global SfM, incremental SfM, UAV-HSfM, UAV imagery

CLC Number:

P237

ZHANG Chi, GAN Shu, YUAN Xiping, LUO Weidong, MA Chong, LI Yi. UAV-HSfM modeling method and its empirical analysis in the complex mountainous areas of central Yunnan Plateau[J]. Bulletin of Surveying and Mapping, 2025, 0(11): 47-52,61.

References

[1] 姜三,马一尘,李清泉,等.无序无人机影像的并行化SfM三维重建方法[J].测绘学报,2024,53(5):946-958.
[2] 李劲澎,姜挺,肖达,等.基于关系图的无人机影像三维重建[J].光学精密工程,2016,24(6):1501-1509.
[3] 李文达,闫启明,张尚弘,等.基于无人机和SfM的地貌数据采集精度影响研究[J].应用基础与工程科学学报,2019,27(6):1225-1234.
[4] 姜三,刘凯,李清泉,等.融合深度特征的无人机影像SfM重建[J].测绘学报,2024,53(2):321-331.
[5] 许志华,吴立新,刘军,等.顾及影像拓扑的SfM算法改进及其在灾场三维重建中的应用[J].武汉大学学报(信息科学版),2015,40(5):599-606.
[6] 陈武,姜三,李清泉,等.无人机影像增量式运动恢复结构研究进展[J].武汉大学学报(信息科学版),2022,47(10):1662-1674.
[7] 吴宇豪,曹雪峰,彭锦超,等.基于运动结构图的无人机序列影像三维重建[J].系统仿真学报,2020,32(6):1094-1102.
[8] 于英,张永生,薛武,等.一种稳健性增强和精度提升的增量式运动恢复结构方法[J].测绘学报,2019,48(2):207-215.
[9] CHATTERJEE A,GOVINDU V M.Efficient and robust large-scale rotation averaging[C]//Proceedings of 2013 IEEE International Conference on Computer Vision.Sydney:IEEE,2013:521-528.
[10] 郝豪杰,李朝奎,方军,等.改进的AKAZE特征提取的全局式SFM三维重建算法[J].测绘科学技术学报,2021,38(5):492-498.
[11] 许彪,董友强,张力,等.分区优化混合SfM方法[J].测绘学报,2022,51(1):115-126.
[12] 屈文虎,刘振东,蔡昊琳,等.顾及场景连通性的混合式SfM方法[J].测绘学报,2023,52(6):966-979.
[13] 张力,刘玉轩,孙洋杰,等.数字航空摄影三维重建理论与技术发展综述[J].测绘学报,2022,51(7):1437-1457.
[14] 姜三,许志海,张峰,等.面向无人机倾斜影像的高效SfM重建方案[J].武汉大学学报(信息科学版),2019,44(8):1153-1161.
[15] 张茂正,燕宁娜,袁宜红,等.复杂异形古建下基于SfM的视频帧影像三维重构方法[J].测绘通报,2023(10):54-60.
[16] SEITZ S M,DYER C R.Photorealistic scene reconstruction by voxel coloring[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.1997,San Juan:IEEE,2002:1067-1073.
[17] CUI Zhaopeng,TAN Ping.Global structure-from-motion by similarity averaging[C]//Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV).Santiago:IEEE,2015:864-872.
[18] 于广瑞,刘兴春,时春霖,等.一种稳健的无人机影像三维重建方法[J].测绘通报,2022(6):76-81.
[19] 蔡嘉伦,贾洪果,刘国祥,等.对比传统低空航测的无人机倾斜摄影测量精度评估[J].测绘通报,2022(2):31-36.
[20] 高莎,袁希平,甘淑,等.基于无人机成像点云的禄丰恐龙谷南缘环状地貌空间特征探测试验分析[J].地质科技通报,2021,40(6):283-292.