Oblique Image Based Automatic Aerotriangulation and Its Application in 3D City Model Reconstruction
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摘要: 在一种具有视点不变性的改进的倾斜影像自动匹配方法的基础上,针对多视倾斜影像光束法平差方法中未知参数多的问题(例如可能导致平差解算不稳定),提出了带有相对姿态参数的倾斜影像光束法平差模型,并给出了该模型的适用范围。对一套典型的数字倾斜摄影仪SWDC-5的倾斜影像数据进行实验,结果表明:当相机之间曝光延迟小,且相对姿态参数可以看作比较严格的刚体时,该方法的自动空中三角测量(简称空三)处理精度较高,单位权中误差为0.46像素,像点平均残差为0.27像素。将该空三成果应用到城市真三维模型重建中,计算机自动得到的三维表面模型纹理比较自然、真实,能够满足一定程度的视觉需求,为大规模城市真三维模型重建提供了参考。Abstract: In this paper, we proposed an automatic aerotriangulation method based on SWDC-5 oblique images. First of all, we designed an improved viewpoint-invariant matching method for oblique images based on the perspective transformation. Secondly, in order to reduce the amount of unknown adjustment parameters (overmuch unknown adjustment parameter may weaken the instability of adjustment solution), we offered a new bundle adjustment model for oblique images which took the relative attitude parameters of cameras into account, and also gave the application scope of the model. Experiments conducted on the typical SWDC-5 oblique images demonstrated that when the relative attitude of cameras (on same camera station) are stable and their cameras exposure are limited within a short time delay, the aero-triangulation accuracy of our method is high, the unit weight error is 0.46 pixels and the average residual of image points is 0.27 pixels. Thirdly, we applied the results of aero-triangulation to PMVS (patch-based multi-view stereo matching) algorithm to gain the dense point-cloud of the experimental city, used screened Poisson reconstruction algorithm to get its 3D mesh, and reconstruct its 3D surface with 3D texture algorithm. Experiments showed that the accuracy of the aero triangulation met the requirements of applications, and the obtained 3D city model had a natural, real texture, all this proved that the idea of automatic reconstruction of 3D city model is feasible and made a good reference for the large-scale 3D city model reconstruction.
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图 5 参与空三的点云(约106个连接点)
Figure 5. Point-Cloud of Aerotriangulation (Around 1 Million Tie-Points)
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图 6 平差方法1(蓝)与平差方法2(红)的人工刺点侧视与下视坐标差值
Figure 6. Coordinate Differences Between Side-view Image Forward Intersection and Bottom-view Image Forward Intersection for 25 Checkpoints (Blue: the Result of Bundle Adjustment Model 1,Red: the Result of Bundle Adjustment Model 2)
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图 7 五镜头共15张影像的密集三维点云
Figure 7. 3D Dense Point-Cloud of the Test Area (from Fifteen Oblique Images)
表 1 实验影像数据关系
Table 1 Relationship Between Experimental Image and Its Bottom-View Image at the Same Camera Station
相机名 影像名 单视影像张数/张 对应同时刻曝光下视影像名 对应航带 A相机 07025AR0031~36；05020AR0031~40 16 07025ER0031~36；05020ER0031~40 25；20 B相机 07023BR0036~42；10022BR0035~42 15 07023ER0036~42；10022ER0035~42 23；22 C相机 07024CR0043~50；05021CR0031~38 16 07024ER0043~50；05021ER0031~38 24；21 D相机 07023DR0023~32；10022DR0027~32 13 07023ER0023~32；10022ER0027~32 23；22 E相机 07023ER0031~37；10022ER0032~37 13 07023ER0031~37；10022ER0032~37 23；22 总张数/张 73 65 
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表 2 两种光束法平差模型的空三精度比较(像元大小为6 μm)
Table 2 Comparison of Aerotriangulation Accuracy for Two Bundle Adjustment Models (Pixel Size: 6 μm)
平差方法1 平差方法2 迭代次数/次 3 3 单位权中误差/μm 2.740 505 2.743 400 像点最大残差/μm 11.2 11.8 像点平均残差/μm 1.610 909 1.612 831 ΔX最大值/m 0.518 332 0.523 301 ΔX平均值/m 0.117 331 0.118 542 ΔY最大值/m 0.233 792 0.236 428 ΔY平均值/m 0.094 365 0.101 304 ΔZ最大值/m 0.500 458 0.506 545 ΔZ平均值/m 0.160 64 0.164 913
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表 3 侧视(左/右、前/后)相机与下视相机之间的相对姿态参数平差前后对比
Table 3 Comparison of the Relative Attitude Parameters Between the Two Side- and Bottom- View Cameras Before and After Bundle Adjustment
相对姿态参数 A相机-E相机 C相机-E相机 D相机-E相机 B相机-E相机 初始值 差值 初始值 差值 初始值 差值 初始值 差值 X/m 0.146 -0.051 4 -0.111 0.058 5 -0.027 0.242 8 0.045 -0.236 9 Y/m -0.004 -0.003 9 -0.014 0.027 2 -0.121 0.268 2 0.149 0.186 3 Z/m 0.003 -0.022 9 0.037 -0.006 9 0.018 -0.293 8 0.026 -0.333 4 φ/(°) -44.800 2 0.000 0 44.913 33 0.000 0 -1.437 79 -0.004 3 0.230 507 -0.010 7 w/(°) -0.581 43 0.000 0 0.499 547 0.000 0 45.147 42 0.038 7 -44.607 8 -0.033 4 κ/(°) 90.153 45 0.000 0 -89.844 4 0.000 0 1.183 133 0.015 7 -179.615 0.004 9 注：差值=结算值-初始值。
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表 4 两种平差模型的优点和适用范围
Table 4 Advantages and Adoption Scopes of the Two Bundle Adjustment Models
优点 适用范围 平差方法1 解算精度高，适用范围广 相对姿态变化，各视相机曝光延迟大 平差方法2 未知数少，解算更加稳定 相对姿态稳定，且各视相机曝光延迟小
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