An improved block dense matching method based on depth map

doi:10.13474/j.cnki.11-2246.2023.0044

Abstract

Abstract: Oblique photography modeling often requires hundreds or even tens of thousands of images and usually requires block dense matching reconstruction. However, the commonly used block dense matching method can only model each block separately and the repeated images between blocks will participate in the matching calculation multiple times, resulting in redundancy and time-consuming calculations. Therefore, an improved block dense matching method is proposed. First, the image association algorithm is used to calculate the neighborhood image set for each image in the scene and the reconstruction state object of the image is initialized at the same time. Then, the entire scene is automatically clustered in space to complete the block and the sub-blocks needed for reconstruction are extracted. Finally, according to the image reconstruction state, object estimation, filtering depth map and depth map fusion generate dense point cloud. Experimental results show that this method can significantly improve the efficiency of oblique image 3D reconstruction.

Key words: oblique photography, modeling, block, dense matching, 3D reconstruction

CLC Number:

WANG Yan, ZHANG Tingting, WANG Fei, LIU Zhendong. An improved block dense matching method based on depth map[J]. Bulletin of Surveying and Mapping, 2023, 0(2): 78-83.

References

[1] 俞建人,李涛,申淑娟,等.倾斜摄影三维模型的房檐改正方法分析与精度评价[J].测绘通报,2017(5):75-78.
[2] 徐思奇,黄先锋,张帆,等.倾斜摄影测量技术在大比例尺地形图测绘中的应用[J]. 测绘通报, 2018(2): 111-115.
[3] SCHONBERGER J L, FRAHM J M. Structure-from-motion revisited[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 4104-4113.
[4] 艾海滨,董友强,王庆栋,等.基于语义的倾斜摄影单体建模技术研究[J].测绘科学,2019,44(6):374-379.
[5] 张小青,侯妙乐,朱光.网格模型的文物缺损面积计算[J]. 测绘科学,2013,38(4):144-148.
[6] 冯鸣,杨明龙,夏永华,等.三维激光扫描与倾斜摄影测量的高陡崖三维建模[J].测绘科学,2020,45(1):99-107.
[7] 王慧,王利勇,闫科,等.无序航空遥感影像的三维地形重建[J].测绘科学技术学报,2021,38(1):33-38.
[8] 赵明衍,戴晨光,狄亚南,等.一种POS数据辅助多视角倾斜航空影像匹配方法[J].测绘科学技术学报,2016,33(4):431-435.
[9] 姜三.无人机倾斜影像高效SfM重建关键技术研究[D].武汉:武汉大学,2018.
[10] MOSTEGEL C, PRETTENTHALER R, FRAUNDORFER F, et al. Scalable surface reconstruction from point clouds with extreme scale and density diversity[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu:IEEE, 2017:1-14.
[11] HAN Jiali, SHEN Shuhan. Scalable point cloud meshing for image-based large-scale 3D modeling[J].Visual Computing for Industry, Biomedicine, and Art, 2019, 2(1):10.
[12] SNAVELY N, SEITZ S, SZELISKI R. Skeletal graphs for efficient structure from motion[C]// Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage: IEEE, 2008:1-8.
[13] FURUKAWA Y, CURLESS B, SEITZ S.Towards internet-scale multi-view stereo[C]// Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco: IEEE, 2010:1434-1441.
[14] 刘彬,陈向宁,郭连朋.分块立体重建的PMVS算法研究与实现[J].测绘科学技术学报,2014,31(6):614-619.
[15] 倪标,王铮尧.一种倾斜影像密集匹配算法的设计与实现[J].地理空间信息,2020,18(7):66-69.
[16] 薛俊诗,易辉,吴止锾,等.一种基于场景图分割的混合式多视图三维重建方法自动化[J].测绘科学技术学报,2020,46(4):782-795.
[17] GALLIANI S, LASINGER K, SCHINDLER K. Massively parallel multiview stereopsis by surface normal diffusion[C]// Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015:873-881.