Automated Aerial Triangulation (AAT), aiming to restore image pose and reconstruct sparse points simultaneously, plays a pivotal role in earth observation. With its rich research heritage spanning several decades in photogrammetry, AAT has evolved into a fundamental process widely applied in large-scale Unmanned Aerial Vehicle (UAV) based mapping. Despite its advancements, 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 DeepAAT's substantial improvements over conventional AAT methods, highlighting its potential in the efficiency and accuracy of UAV-based 3D reconstruction tasks. To benefit the photogrammetry society, the code of DeepAAT will be released at: https://github.com/WHU-USI3DV/DeepAAT.

翻译：自动化空中三角测量旨在同时恢复影像姿态与重建稀疏点云，在地球观测领域具有核心地位。历经摄影测量学界数十年深厚研究积淀，AAT已发展为大规模无人机测绘中的基础性流程。尽管取得显著进展，经典AAT方法仍面临效率低下与鲁棒性不足等挑战。本文提出DeepAAT——专为无人机影像AAT设计的深度学习网络。该网络综合利用影像的空间与光谱特征，增强其解决错误匹配对与精准预测影像姿态的能力。DeepAAT标志着AAT效率的重大突破，在确保场景完整覆盖与精度的同时，其处理速度相比增量式AAT方法提升数百倍、全局式AAT方法提升数十倍，且重建精度相当。此外，DeepAAT的场景聚类与合并策略使得在计算资源受限条件下仍能实现大规模无人机影像的快速定位与姿态解算。实验结果表明，DeepAAT相较传统AAT方法取得显著提升，凸显其在无人机三维重建任务效率与精度方面的潜力。为惠及摄影测量学界，DeepAAT代码将发布于：https://github.com/WHU-USI3DV/DeepAAT