SfM (Structure from Motion) has been extensively used for UAV (Unmanned Aerial Vehicle) image orientation. Its efficiency is directly influenced by feature matching. Although image retrieval has been extensively used for match pair selection, high computational costs are consumed due to a large number of local features and the large size of the used codebook. Thus, this paper proposes an efficient match pair retrieval method and implements an integrated workflow for parallel SfM reconstruction. First, an individual codebook is trained online by considering the redundancy of UAV images and local features, which avoids the ambiguity of training codebooks from other datasets. Second, local features of each image are aggregated into a single high-dimension global descriptor through the VLAD (Vector of Locally Aggregated Descriptors) aggregation by using the trained codebook, which remarkably reduces the number of features and the burden of nearest neighbor searching in image indexing. Third, the global descriptors are indexed via the HNSW (Hierarchical Navigable Small World) based graph structure for the nearest neighbor searching. Match pairs are then retrieved by using an adaptive threshold selection strategy and utilized to create a view graph for divide-and-conquer based parallel SfM reconstruction. Finally, the performance of the proposed solution has been verified using three large-scale UAV datasets. The test results demonstrate that the proposed solution accelerates match pair retrieval with a speedup ratio ranging from 36 to 108 and improves the efficiency of SfM reconstruction with competitive accuracy in both relative and absolute orientation.

翻译：SfM（运动恢复结构）已被广泛用于无人机影像定向，其效率直接受特征匹配影响。尽管图像检索已被大量用于匹配对选择，但由于局部特征数量庞大且所用码本规模较大，导致计算成本较高。因此，本文提出一种高效的匹配对检索方法，并实现面向并行SfM重建的集成工作流。首先，考虑无人机影像及其局部特征的冗余性，在线训练个体化码本，避免从其他数据集训练码本带来的歧义性。其次，利用训练好的码本通过VLAD（局部聚合描述子向量）聚合将每幅影像的局部特征聚合成单一高维全局描述子，显著减少特征数量及图像索引中最近邻搜索的负担。然后，基于HNSW（分层可导航小世界）图结构对全局描述子建立索引以进行最近邻搜索，并通过自适应阈值选择策略检索匹配对，用于构建视图图，实现基于分治策略的并行SfM重建。最后，使用三组大规模无人机数据集验证了所提方法的性能。测试结果表明，该方法以36至108倍的加速比提升匹配对检索速度，并在相对定向与绝对定向中保持竞争性精度的同时，显著提高了SfM重建效率。