Automated Aerial Triangulation (AAT), aiming to restore image poses and reconstruct sparse points simultaneously, plays a pivotal role in earth observation. AAT has evolved into a fundamental process widely applied in large-scale Unmanned Aerial Vehicle (UAV) based mapping. However classic AAT methods still face challenges like low efficiency and limited robustness. This paper introduces DeepAAT, a deep learning network designed specifically for AAT of UAV imagery. DeepAAT considers both spatial and spectral characteristics of imagery, enhancing its capability to resolve erroneous matching pairs and accurately predict image poses. DeepAAT marks a significant leap in AAT’s efficiency, ensuring thorough scene coverage and precision. Its processing speed outpaces incremental AAT methods by hundreds of times and global AAT methods by tens of times while maintaining a comparable level of reconstruction accuracy. Additionally, DeepAAT’s scene clustering and merging strategy facilitate rapid localization and pose determination for large-scale UAV images, even under constrained computing resources. The experimental results demonstrate that DeepAAT substantially improves over conventional AAT methods, highlighting its potential for increased efficiency and accuracy in UAV-based 3D reconstruction tasks. To benefit the photogrammetry society, the code of DeepAAT will be released at: https://github.com/WHU-USI3DV/DeepAAT.

中文翻译：

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DeepAAT：深度自动航空三角测量，用于基于无人机的快速测绘


自动航空三角测量 （AAT） 旨在同时恢复图像位姿和重建稀疏点，在地球观测中起着关键作用。AAT 已发展成为一种基本过程，广泛应用于基于大规模无人机 （UAV） 的测绘。然而，经典的 AAT 方法仍然面临效率低和稳定性有限的挑战。本文介绍了 DeepAAT，这是一种专为无人机图像的 AAT 设计的深度学习网络。DeepAAT 同时考虑图像的空间和光谱特征，增强了其解析错误匹配对和准确预测图像姿势的能力。DeepAAT 标志着 AAT 效率的重大飞跃，确保了全面的场景覆盖和精度。其处理速度是增量 AAT 方法的数百倍，是全局 AAT 方法的数十倍，同时保持了相当的重建精度水平。此外，DeepAAT 的场景聚类和合并策略有助于快速定位和确定大规模无人机图像的姿态，即使在计算资源受限的情况下也是如此。实验结果表明，DeepAAT 比传统的 AAT 方法有了显着改进，凸显了它在基于无人机的 3D 重建任务中提高效率和准确性的潜力。为造福摄影测量学会，DeepAAT 代码将于以下网址发布：https://github.com/WHU-USI3DV/DeepAAT。