ABSTRACT

Image matching is critical for photorealistic 3D reconstruction based on oblique images. An efficient match pair selection methodology for matching large-scale oblique UAV images is proposed in this paper. The proposed methodology effectively uses existing geospatial data and prior knowledge about data acquisition to generate precise match pairs. First, the principal point of each image is directly georeferenced. The direct georeferencing is based on a novel new DEM-aided direct georeferencing algorithm which employs an iterative binary search in the elevation domain. Second, a terrain-adaptive search radius is calculated for each image based on the calculated ground points, POS data, and prior knowledge. Finally, initial match pairs are generated based on a camera-oriented approach. The camera-oriented approach generates match pairs using k nearest neighbours (knn) search and prior knowledge. False connections in the initial match pairs are filtered using geometrical constraints. A limited number of match pairs are kept to reduce the redundancy of the generated match graph. The proposed methodology is tested on a dataset containing 28,560 images. The experimental results show that the precision of the generated match pairs is 99.99%. An incremental 3D reconstruction of the scene is conducted based on the robustly matched images. More than 99.8% of all the images are successfully oriented. The profiling analysis shows that the proposed methodology generates all the match pairs in 7.719 seconds. The precision, efficiency, and robustness of the proposed methodology are comprehensively analysed. The experimental and simulation results show that the proposed methodology is efficient and precise for large-scale UAV photogrammetry.

摘要

图像匹配对于基于倾斜图像的逼真 3D 重建至关重要。本文提出了一种用于匹配大尺度倾斜无人机图像的有效匹配对选择方法。所提出的方法有效地使用现有的地理空间数据和有关数据采集的先验知识来生成精确的匹配对。首先，每个图像的主要点都直接进行地理配准。直接地理配准基于新的 DEM 辅助直接地理配准算法，该算法在高程域中采用迭代二分搜索。其次，根据计算出的地面点、POS 数据和先验知识，为每个图像计算地形自适应搜索半径。最后，基于面向相机的方法生成初始匹配对。面向相机的方法使用 k 个最近邻 (knn) 搜索和先验知识生成匹配对。使用几何约束过滤初始匹配对中的错误连接。保留有限数量的匹配对以减少生成的匹配图的冗余。所提出的方法在包含 28,560 张图像的数据集上进行了测试。实验结果表明，生成的匹配对的准确率为99.99%。基于稳健匹配的图像进行场景的增量 3D 重建。超过 99.8% 的图像都被成功定向。剖析分析表明，所提出的方法在 7.719 秒内生成所有匹配对。综合分析了所提出方法的精度、效率和稳健性。