This paper presents an approach for Structure from Motion (SfM) for unorganized complex image sets. To achieve high accuracy and robustness, image triplets are employed and an (approximate) internal camera calibration is assumed to be known. The complexity of an image set is determined by the camera configurations which may include wide as well as weak baselines.

Wide baselines occur for instance when terrestrial images and images from small Unmanned Aerial Systems (UAS) are combined. The resulting large (geometric/radiometric) distortions between images make image matching difficult possibly leading to an incomplete result. Weak baselines mean an insufficient distance between cameras compared to the distance of the observed scene and give rise to critical camera configurations. Inappropriate handling of such configurations may lead to various problems in triangulation-based SfM up to total failure.

The focus of our approach lies on a complete linking of images even in case of wide or weak baselines. We do not rely on any additional information such as camera configurations, Global Positioning System (GPS) or an Inertial Navigation System (INS). As basis for generating suitable triplets to link the images, an iterative graph-based method is employed formulating image linking as the search for a terminal Steiner minimum tree in the line graph. SIFT (Lowe, 2004) descriptors are embedded into Hamming space for fast image similarity ranking. This is employed to limit the number of pairs to be geometrically verified by a computationally and more complex wide baseline matching method (Mayer et al., 2012). Critical camera configurations which are not suitable for geometric verification are detected by means of classification (Michelini and Mayer, 2019). Additionally, we propose a graph-based approach for the optimization of the hierarchical merging of triplets to efficiently generate larger image subsets.

By this means, a complete, 3D reconstruction of the scene is obtained. Experiments demonstrate that the approach is able to produce reliable orientation for large image sets comprising wide as well as weak baseline configurations.

本文提出了一种针对无组织的复杂图像集的运动结构（SfM）方法。为了实现高精度和鲁棒性，采用了图像三重态并且假定（近似）内部摄像机校准是已知的。图像集的复杂性由相机配置决定，其中可能包括较宽的基线和较弱的基线。

例如，当将地面图像和小型无人机系统（UAS）的图像合并在一起时，基准线就很宽。图像之间产生的大（几何/辐射）失真使图像匹配变得困难，可能导致结果不完整。基线较弱意味着相较于所观察场景的距离，摄像机之间的距离不足，并且会导致关键的摄像机配置。对此类配置的不当处理可能会导致基于三角剖分的SfM中出现各种问题，甚至完全失败。

即使基线较宽或较弱，我们方法的重点也在于图像的完整链接。我们不依赖任何其他信息，例如摄像机配置，全球定位系统（GPS）或惯性导航系统（INS）。作为生成合适的三元组以链接图像的基础，采用了基于迭代图的方法，该方法将图像链接公式化为对折线图中终端Steiner最小树的搜索。SIFT（Lowe，2004）描述符被嵌入到汉明空间中，以实现快速图像相似性排名。这用于限制要通过计算和更复杂的宽基线匹配方法进行几何验证的对的数量（Mayer等人，2012）。通过分类检测不适合几何验证的关键相机配置（Michelini和Mayer，2019）。此外，我们提出了一种基于图的方法来优化三联体的层次合并以有效生成较大的图像子集。

通过这种方式，可以获得场景的完整3D重建。实验表明，该方法能够为包含宽基线配置和弱基线配置的大型图像集提供可靠的方向。