This paper primarily presents a parallel incremental Structure from Motion (ISfM) solution for large-scale images captured by unmanned aerial vehicles (UAVs). The core ideas are a local connection-constrained edge weighting strategy for match graph construction and an anchor-free parallel merging algorithm for the merged model generation. First, an effective algorithm is employed to retrieve spatially overlapped match pairs, utilizing the global descriptor for image representation and the graph indexing for nearest neighbor searching. Second, match pairs are used to create an undirected weighted match graph that is weighted by the local connection strength of the image. This match graph is then used to achieve parallel ISfM through graph clustering. Third, an anchor-free cluster merging algorithm, called AFP-Merging, is then designed by taking advantage of the independent connection between clusters, which increases the merging efficiency and stability. For robust estimation, AFP-Merging is implemented via a bidirectional mean square reprojection error. Finally, extensive evaluation and analysis have been carried out to verify its validation using large-scale UAV datasets captured from classical oblique photogrammetry and recent optimized views photogrammetry. Experiment results show that the proposed solution can generate more compact scene clusters and achieve a speedup ratio greater than 9.0 in cluster merging; compared with recent parallel ISfM, its orientation accuracy is higher in both relative bundle adjustment (BA) without GCPs (Ground Control Points) and absolute BA with GCPs. For the orientation of very large-scale UAV images, it has been successfully applied to a dataset containing ninety thousand images over an area of 50.0 . The proposed method provides a more efficient and reliable parallel SfM solution. The executable tool is made publicly available.

中文翻译：

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通过无锚并行合并实现无人机图像运动的高效结构

本文主要提出了一种针对无人机（UAV）捕获的大规模图像的并行增量运动结构（ISfM）解决方案。核心思想是用于匹配图构建的局部连接约束边缘加权策略和用于合并模型生成的无锚并行合并算法。首先，采用有效的算法来检索空间重叠的匹配对，利用用于图像表示的全局描述符和用于最近邻搜索的图索引。其次，匹配对用于创建无向加权匹配图，该匹配图由图像的局部连接强度加权。然后使用该匹配图通过图聚类实现并行 ISfM。第三，利用簇之间的独立连接，设计了一种无锚簇合并算法，称为AFP-Merging，提高了合并效率和稳定性。为了进行鲁棒估计，AFP 合并是通过双向均方重投影误差来实现的。最后，使用从经典倾斜摄影测量和最近优化的视图摄影测量捕获的大规模无人机数据集进行了广泛的评估和分析，以验证其有效性。实验结果表明，该方案能够生成更紧凑的场景簇，并且在簇合并中实现大于9.0的加速比；与最近的并行 ISfM 相比，无论是无 GCP（地面控制点）的相对束平差（BA）还是有 GCP 的绝对 BA，其定向精度都更高。对于超大规模无人机图像的定向，它已成功应用于包含 50.0 区域内的九万张图像的数据集。所提出的方法提供了一种更高效、更可靠的并行 SfM 解决方案。可执行工具是公开可用的。