Recent Research of Incremental Structure from Motion for Unmanned Aerial Vehicle Images

CHEN Wu; JIANG San; LI Qingquan; JIANG Wanshou

doi:10.13203/j.whugis20220130

Volume 47 Issue 10

Oct. 2022

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Geomatics and Information Science of Wuhan University > 2022 > 47(10): 1662-1674. > DOI: 10.13203/j.whugis20220130

CHEN Wu, JIANG San, LI Qingquan, JIANG Wanshou. Recent Research of Incremental Structure from Motion for Unmanned Aerial Vehicle Images[J]. Geomatics and Information Science of Wuhan University, 2022, 47(10): 1662-1674. DOI: 10.13203/j.whugis20220130

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Recent Research of Incremental Structure from Motion for Unmanned Aerial Vehicle Images

CHEN Wu^1,,
JIANG San^{1, 2, ,},
LI Qingquan³,
JIANG Wanshou⁴

1.
Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong 999077, China
2.
School of Computer Science, China University of Geosciences(Wuhan), Wuhan 430074, China
3.
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
4.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Funds:

The National Natural Science Foundation of China 42001413

the Natural Science Foundation of Hubei Province 2020CFB324

Hong Kong Scholars Program 2021-114

More Information

Author Bio:
CHEN Wu, PhD, professor, specializes in the theoretical and practical research on GNSS positioning and navigation, and SLAM-based 3D reconstruction. E-mail: wu.chen@polyu.edu.hk
Corresponding author:
JIANG San, PhD, associate professor. E-mail: jiangsan@cug.edu.cn
Received Date: March 29, 2022
Available Online: April 13, 2022
Published Date: October 04, 2022

Graphical Abstract

Abstract

Abstract

Objectives Incremental structure from motion (SfM) has become the widely used workflow for aerial triangulation (AT) of unmanned aerial vehicle (UAV) images. Recently, extensive research has been conducted to improve the efficiency, precision and scalability of SfM-based AT for UAV images. Meanwhile, deep learning-based methods have also been exploited for the geometry processing in the fields of photogrammetry and computer vision, which have been verified with large potential in the AT of UAV images. This paper aims to give a review of recent work in the SfM-based AT for UAV images.
Methods Firstly, the workflow of SfM-based AT is briefly presented in terms of feature matching and geometry solving, in which the former aims to obtain enough and accurate correspondences, and the latter attempts to solve unknown parameters. Secondly, literature review is given for feature matching and geometry solving. For feature matching, classical hand-crafted and recent learning-based methods are presented from the aspects of feature extraction, feature matching and outlier removal. For geometry solving, the principle of SfM-based AT is firstly given with some well-known and widely-used open-source SfM software. Efficiency improvement and large-scale processing are then summarized, which focus on improving the capability of SfM to process large-scale UAV images. Finally, further search is concluded from four aspects, including the change of data acquisition modes, the scalability for large-scale scenes, the development of communication and hardware, and the fusion of deep learning-based methods.
Results The review demonstrates that the existing research promotes the development of SfM-based AT towards the direction of high efficiency, high precision and high robustness, and also promotes the development of both commercial and open-source software packages.
Conclusions Considering the characteristics of UAV images, the efficiency, precision and robustness of SfM-based AT and its application need further improvement and exploitation. This paper could give an extensive conclusion and be a useful reference to the related researchers.
- unmanned aerial vehicle (UAV) images,
- aerial triangulation,
- structure from motion (SfM),
- feature matching,
- deep learning

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References

[1]	李德仁, 李明. 无人机遥感系统的研究进展与应用前景[J]. 武汉大学学报·信息科学版, 2014, 39(5): 505-513 doi: 10.13203/j.whugis20140045 Li Deren, Li Ming. Research Advance and Application Prospect of Unmanned Aerial Vehicle Remote Sensing System[J]. Geomatics and Information Science of Wuhan University, 2014, 39(5): 505-513 doi: 10.13203/j.whugis20140045
[2]	Colomina I, Molina P. Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 92: 79-97 doi: 10.1016/j.isprsjprs.2014.02.013
[3]	Zhou X H, Xie K, Huang K, et al. Offsite Aerial Path Planning for Efficient Urban Scene Reconstruction[J]. ACM Transactions on Graphics, 2020, 39(6): 192
[4]	Jiang S, Jiang W S, Wang L Z. Unmanned Aerial Vehicle-Based Photogrammetric 3D Mapping: A Survey of Techniques, Applications, and Challenges[J]. IEEE Geoscience and Remote Sensing Magazine, 2248, (99): 2-38
[5]	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 doi: 10.13203/j.whugis20180030 Jiang San, Xu Zhihai, Zhang Feng, et al. Solution for Efficient SfM Reconstruction of Oblique UAV Images[J]. Geomatics and Information Science of Wuhan University, 2019, 44(8): 1153-1161 doi: 10.13203/j.whugis20180030
[6]	薛武, 张永生, 赵玲, 等. 增量式SfM与POS辅助光束法平差精度比较[J]. 测绘学报, 2017, 46(2): 198-207 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201702009.htm Xue Wu, Zhang Yongsheng, Zhao Ling, et al. Comparison of the Accuracy of Incremental SfM with POS-Aided Bundle Adjustment[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(2): 198-207 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201702009.htm
[7]	Fan B, Kong Q Q, Wang X C, et al. A Performance Evaluation of Local Features for Image-Based 3D Reconstruction[J]. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 2019, 28(10): 4774-4789 doi: 10.1109/TIP.2019.2909640
[8]	Triggs B, McLauchlan P F, Hartley R I, et al. Bundle Adjustment — A Modern Synthesis[M]//Vision Algorithms: Theory and Practice. Berlin, Heidelberg: Springer, 2000: 298-372
[9]	Bhowmick B, Patra S, Chatterjee A, et al. Divide and Conquer: A Hierarchical Approach to Large-Scale Structure-from-Motion[J]. Computer Vision and Image Understanding, 2017, 157: 190-205 doi: 10.1016/j.cviu.2017.02.006
[10]	Zhu S Y, Shen T W, Zhou L, et al. Accurate, Scalable and Parallel Structure from Motion[EB/ OL]. 2017: arXiv: 1702. 08601. https://arxiv.org/abs/1702.08601
[11]	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 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201902011.htm Yu Ying, Zhang Yongsheng, Xue Wu, et al. A Incremental Structure from Motion Method of Robustness Enhancement and Accuracy Improvement[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(2): 207-215 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201902011.htm
[12]	Harris C, Stephens M. A Combined Corner and Edge Detector[C]//Proceedings of the Alvey Vision Conference, Manchester, UK, 1988
[13]	Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110 doi: 10.1023/B:VISI.0000029664.99615.94
[14]	Morel J M, Yu G S. ASIFT: A New Framework for Fully Affine Invariant Image Comparison[J]. SIAM Journal on Imaging Sciences, 2009, 2(2): 438-469 doi: 10.1137/080732730
[15]	Arandjelović R, Zisserman A. Three Things Everyone Should Know to Improve Object Retrieval[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012
[16]	Bay H, Ess A, Tuytelaars T, et al. Speeded-up Robust Features(SURF)[J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359 doi: 10.1016/j.cviu.2007.09.014
[17]	Rublee E, Rabaud V, Konolige K, et al. ORB: An Efficient Alternative to SIFT or SURF[C]//2011 International Conference on Computer Vision, Barcelona, Spain, 2011
[18]	Wu C C. SiftGPU: A GPU Implementation of Scale Invariant Feature Transform (SIFT)[EB/OL]. [2007-08-12]. https://github.com/pitzer/SiftGPU
[19]	闫利, 费亮, 叶志云, 等. 大范围倾斜多视影像连接点自动提取的区域网平差法[J]. 测绘学报, 2016, 45(3): 310-317 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201603011.htm Yan Li, Fei Liang, Ye Zhiyun, et al. Automatic Tie-Points Extraction for Triangulation of Large-Scale Oblique Multi-View Images[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(3): 310-317 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201603011.htm
[20]	杨化超, 张书毕, 张秋昭. 基于SIFT的宽基线立体影像最小二乘匹配方法[J]. 测绘学报, 2010, 39(2): 187-194 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201002017.htm Yang Huachao, Zhang Shubi, Zhang Qiuzhao. Least Squares Matching Methods for Wide Base-Line Stereo Images Based on SIFT Features[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(2): 187-194 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201002017.htm
[21]	于英, 张永生, 薛武, 等. 影像连接点均衡化高精度自动提取[J]. 测绘学报, 2017, 46(1): 90-97 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201701014.htm Yu Ying, Zhang Yongsheng, Xue Wu, et al. Automatic Tie Points Extraction with Uniform Distribution and High Precision[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(1): 90-97 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201701014.htm
[22]	詹总谦, 李一挥, 王陈东, 等. 顾及局部相对几何变形改正的影像匹配和空三逐步精化方法[J]. 武汉大学学报·信息科学版, 2018, 43(11): 1620-1627 doi: 10.13203/j.whugis20170342 Zhan Zongqian, Li Yihui, Wang Chendong, et al. A Stepwise Refinement Method for Image Matching and Aerotriangulation Using Correction of Local Relative Geometric Distortion[J]. Geomatics and Information Science of Wuhan University, 2018, 43(11): 1620-1627 doi: 10.13203/j.whugis20170342
[23]	Hu H, Zhu Q, Du Z Q, et al. Reliable Spatial Relationship Constrained Feature Point Matching of Oblique Aerial Images[J]. Photogrammetric Engineering & Remote Sensing, 2015, 81(1): 49-58
[24]	Jiang S, Jiang W S. On-Board GNSS/IMU Assis-ted Feature Extraction and Matching for Oblique UAV Images[J]. Remote Sensing, 2017, 9(8): 813 doi: 10.3390/rs9080813
[25]	Sun Y B, Zhao L, Huang S D, et al. L2-SIFT: SIFT Feature Extraction and Matching for Large Images in Large-Scale Aerial Photogrammetry[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 91: 1-16 doi: 10.1016/j.isprsjprs.2014.02.001
[26]	张力, 艾海滨, 许彪, 等. 基于多视影像匹配模型的倾斜航空影像自动连接点提取及区域网平差方法[J]. 测绘学报, 2017, 46(5): 554-564 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201705005.htm Zhang Li, Ai Haibin, Xu Biao, et al. Automatic Tie-Point Extraction Based on Multiple-Image Matching and Bundle Adjustment of Large Block of Oblique Aerial Images[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(5): 554-564 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201705005.htm
[27]	Schönberger J L, Hardmeier H, Sattler T, et al. Comparative Evaluation of Hand-Crafted and Learned Local Features[C]//IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017
[28]	Tian Y R, Fan B, Wu F C. L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space[C]//IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017
[29]	Mishchuk A, Mishkin D, Radenović F, et al. Working Hard to Know Your Neighbor s Margins: Local Descriptor Learning Loss[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017
[30]	Luo Z X, Shen T W, Zhou L, et al. GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints[C]//Proceedings of the European Conference on Computer Vision (ECCV), Munich, German,, 2018
[31]	Luo Z X, Shen T W, Zhou L, et al. ContextDesc: Local Descriptor Augmentation with Cross-Modality Context[C]//IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019
[32]	Yi K M, Trulls E, Ono Y, et al. Learning to Find Good Correspondences[C]//IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018
[33]	De Tone D, Malisiewicz T, Rabinovich A. SuperPoint: Self-Supervised Interest Point Detection and Description[C]//IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, USA, 2018
[34]	Dusmanu M, Rocco I, Pajdla T, et al. D2-Net: A trainable CNN for Joint Description and Detection of Local Features[C]//IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019
[35]	Arya S, Mount D M, Netanyahu N S, et al. An Optimal Algorithm for Approximate Nearest Neighbor Searching Fixed Dimensions[J]. Journal of the ACM, 1998, 45(6): 891-923 doi: 10.1145/293347.293348
[36]	Hartmann W, Havlena M, Schindler K. Recent Developments in Large-Scale Tie-Point Matching[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 115: 47-62 doi: 10.1016/j.isprsjprs.2015.09.005
[37]	Sedaghat A, Mokhtarzade M, Ebadi H. Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4516-4527 doi: 10.1109/TGRS.2011.2144607
[38]	Wu C C. Towards Linear-Time Incremental Structure from Motion[C]//International Conference on 3D Vision, Seattle, USA, 2013
[39]	Hartmann W, Havlena M, Schindler K. Predicting Matchability[C]//IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014
[40]	Frahm J M, Pollefeys M, Lazebnik S, et al. Fast Robust Large-Scale Mapping from Video and Internet Photo Collections[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(6): 538-549 doi: 10.1016/j.isprsjprs.2010.08.009
[41]	Raguram R, Wu C C, Frahm J M, et al. Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs[J]. International Journal of Computer Vision, 2011, 95(3): 213-239 doi: 10.1007/s11263-011-0445-z
[42]	Alsadik B, Gerke M, Vosselman G, et al. Minimal Camera Networks for 3D Image Based Modeling of Cultural Heritage Objects[J]. Sensors, 2014, 14(4): 5785-5804
[43]	Havlena M, Torii A, Pajdla T. Efficient Structure from Motion by Graph Optimization[C]//European Conference on Computer Vision, Berlin, Germany, 2010
[44]	Jiang S, Jiang W S. Efficient Match Pair Selection for Oblique UAV Images Based on Adaptive Vocabulary Tree[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 161: 61-75
[45]	Schönberger J L, Radenović F, Chum O, et al. From Single Image Query to Detailed 3D Recons-truction[C]//IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015
[46]	Jiang S, Jiang W S. Efficient SfM for Oblique UAV Images: From Match Pair Selection to Geometrical Verification[J]. Remote Sensing, 2018, 10(8): 1246
[47]	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 doi: 10.13203/j.whugis20130444 Xu Zhihua, Wu Lixin, Liu Jun, et al. Modification of SfM Algorithm Referring to Image Topology and Its Application in 3-Dimension Reconstruction of Disaster Area[J]. Geomatics and Information Science of Wuhan University, 2015, 40(5): 599-606 doi: 10.13203/j.whugis20130444
[48]	Jiang S, Jiang W S. Efficient Structure from Motion for Oblique UAV Images Based on Maximal Spanning Tree Expansion[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 132: 140-161
[49]	Xu Z H, Wu L X, Gerke M, et al. Skeletal Camera Network Embedded Structure-from-Motion for 3D Scene Reconstruction from UAV Images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 121: 113-127
[50]	Han X F, Leung T, Jia Y Q, et al. MatchNet: Unifying Feature and Metric Learning for Patch-based Matching[C]//IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015
[51]	Zagoruyko S, Komodakis N. Learning to Compare Image Patches via Convolutional Neural Networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015
[52]	Simo-Serra E, Trulls E, Ferraz L, et al. Discriminative Learning of Deep Convolutional Feature Point Descriptors[C]//IEEE International Conference on Computer Vision, Santiago, Chile, 2015
[53]	Fischler M A, Bolles R C. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography[J]. Commun ACM, 1981, 24(6): 381-395
[54]	Raguram R, Chum O, Pollefeys M, et al. USAC: A Universal Framework for Random Sample Consensus[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 2022-2038
[55]	Lu L P, Zhang Y, Tao P J. Geometrical Consistency Voting Strategy for Outlier Detection in Image Matching[J]. Photogrammetric Engineering & Remote Sensing, 2016, 82(7): 559-570
[56]	Jiang S, Jiang W S. Hierarchical Motion Consistency Constraint for Efficient Geometrical Verification in UAV Stereo Image Matching[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 142: 222-242
[57]	张永军, 王博, 段延松. 一种针对大倾角影像匹配粗差剔除的算法[J]. 武汉大学学报·信息科学版, 2013, 38(10): 1135-1138 http://ch.whu.edu.cn/article/id/2787 Zhang Yongjun, Wang Bo, Duan Yansong. An Algorithm of Gross Error Elimination in Image Matching for Large Rotation Angle Images[J]. Geomatics and Information Science of Wuhan University, 2013, 38 (10): 1135-1138 http://ch.whu.edu.cn/article/id/2787
[58]	袁修孝, 袁巍, 陈时雨. 基于图论的遥感影像误匹配点自动探测方法[J]. 武汉大学学报·信息科学版, 2018, 43 (12): 1854-1860 doi: 10.13203/j.whugis20180154 Yuan Xiuxiao, Yuan Wei, Chen Shiyu. An Automatic Detection Method of Mismatching Points in Remote Sensing Images Based on Graph Theory[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1854-1860 doi: 10.13203/j.whugis20180154
[59]	Aguilar W, Frauel Y, Escolano F, et al. A Robust Graph Transformation Matching for Non-Rigid Registration[J]. Image and Vision Computing, 2009, 27(7): 897-910
[60]	Izadi M, Saeedi P. Robust Weighted Graph Transformation Matching for Rigid and Nonrigid Image Registration[J]. IEEE Transactions on Image Processing, 2012, 21(10): 4369-4382
[61]	Jiang S, Jiang W S. Reliable Image Matching via Photometric and Geometric Constraints Structured by Delaunay Triangulation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 153: 1-20
[62]	Ma J Y, Zhao J, Tian J W, et al. Robust Point Matching via Vector Field Consensus[J]. IEEE Transactions on Image Processing, 2014, 23(4): 1706-1721
[63]	Bian J W, Lin W Y, Matsushita Y, et al. GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence[C]//IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017
[64]	Brachmann E, Krull A, Nowozin S, et al. DSAC—Differentiable RANSAC for Camera Localization [C]//IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017
[65]	Ranftl R, Koltun V. Deep Fundamental Matrix Estimation[C]//Proceedings of the European Conference on Computer Vision (ECCV), Munich, German, 2018
[66]	Zhang J H, Sun D W, Luo Z X, et al. Learning Two-View Correspondences and Geometry Using Order-Aware Network[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, South Korea, 2019
[67]	Sun W W, Jiang W, Trulls E, et al. ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning[C]//IEEE Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020
[68]	Schönberger J L, Frahm J M. Structure-from-Motion Revisited[C]//IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016
[69]	Moulon P, Monasse P, Marlet R. Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion[C]//IEEE International Conference on Computer Vision, Sydney, Australia, 2013
[70]	Moulon P, Monasse P, Marlet R. Adaptive Structure from Motion with Model Estimation[C]//Asian Conference on Computer Vision, Daejeon, Democratic People s Republic of Korea, 2012
[71]	Fuhrmann S, Langguth F, Goesele M. MVE: A Multi-View Reconstruction Environment[C]//Proceedings of the Eurographics Workshop on Graphics and Cultural Heritage, Darmstadt, Germany, 2014
[72]	VisualSFM: A Visual Structure from Motion Sys tem[EB/OL]. [2022-01-30]. http://www.cs.washington.edu/homes/ccwu/vsfm
[73]	Rupnik E, Daakir M, Pierrot Deseilligny M. MicMac − A Free, Open-Source Solution for Photogrammetry[J]. Open Geospatial Data, Software and Standards, 2017, 2: 14
[74]	Snavely N, Seitz S M, Szeliski R. Photo Tourism [J]. ACM Transactions on Graphics, 2006, 25(3): 835-846
[75]	Wu C C, Agarwal S, Curless B, et al. Multicore Bundle Adjustment[C]//IEEE Conference on Com puter Vision and Pattern Recognition, Colorado Springs, USA, 2011
[76]	Hänsch R, Drude I, Hellwich O. Modern Methods of Bundle Adjustment on the GPU[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, 3(3): 43-50
[77]	Rupnik E, Nex F, Remondino F. Automatic Orien tation of Large Blocks of Oblique Images[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013, 40: 299-304
[78]	Sun Y B, Sun H B, Yan L, et al. RBA: Reduced Bundle Adjustment for Oblique Aerial Photogram metry[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 121: 128-142
[79]	Cefalu A, Haala N, Fritsch D. Structureless Bundle Adjustment with Self-Calibration Using Accumulated Constraints[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, 3(3): 3-9
[80]	Lourakis M I A, Argyros A A. SBA: A Software Package for Generic Sparse Bundle Adjustment[J]. ACM Transactions on Mathematical Software, 2009, 36(1): 2
[81]	Konolige K. Sparse Sparse Bundle Adjustment[C]//The British Machine Vision Conference, Aberystwyth, UK, 2010
[82]	Kümmerle R, Grisetti G, Strasdat H, et al. G2O: A General Framework for Graph Optimization[C]//IEEE International Conference on Robotics and Automation, Shanghai, China, 2011
[83]	Zhao L, Huang S D, Sun Y B, et al. ParallaxBA: Bundle Adjustment Using Parallax Angle Feature Parametrization[J]. The International Journal of Robotics Research, 2015, 34(4-5): 493-516
[84]	Bhowmick B, Patra S, Chatterjee A, et al. Divide and Conquer: Efficient Large-Scale Structure from Motion Using Graph Partitioning[C]//Asian Conference on Computer Vision, Singapore, 2014
[85]	Chen Y, Shen S H, Chen Y S, et al. Graph-Based Parallel Large Scale Structure from Motion[J]. Pattern Recognition, 2020, 107: 107537
[86]	Xu B, Zhang L, Liu Y X, et al. Robust Hierarchical Structure from Motion for Large-Scale Unstructured Image Sets[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 181: 367-384
[87]	Farenzena M, Fusiello A, Gherardi R. Structure-and-Motion Pipeline on a Hierarchical Cluster Tree[C]//IEEE International Conference on Computer Vision Workshops, Kyoto, Japan, 2009
[88]	Snavely N, Seitz S M, Szeliski R. Skeletal Gaphs for Efficient Structure from Motion[C]//IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, USA, 2008
[89]	Shah R, Deshpande A, Narayanan P J. Multistage SFM: Revisiting Incremental Structure from Motion[C]//The 2nd International Conference on 3D Vision, Tokyo, Japan, 2014
[90]	张永军, 万一, 史文中, 等. 多源卫星影像的摄影测量遥感智能处理技术框架与初步实践[J]. 测绘学报, 2021, 50(8): 1068-1083 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202108009.htm Zhang Yongjun, Wan Yi, Shi Wenzhong, et al. Technical Framework and Preliminary Practices of Photogrammetric Remote Sensing Intelligent Processing of Multi-Source Satellite Images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8): 1068-1083 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202108009.htm
[91]	Luo Z X, Zhou L, Bai X Y, et al. ASLFeat: Learning Local Features of Accurate Shape and Localization[C]//IEEE Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020
[92]	Jiang S, Jiang W S, Guo B X, et al. Learned Local Features for Structure from Motion of UAV Images: A Comparative Evaluation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 10583-10597
[93]	Wang W, Zhao Y, Han P C, et al. TerrainFusion: Real-Time Digital Surface Model Reconstruction Based on Monocular SLAM[C]//IEEE International Conference on Intelligent Robots and Systems (IROS), Macao, China, 2019

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Recent Research of Incremental Structure from Motion for Unmanned Aerial Vehicle Images

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