DeepAAT: Deep Automated Aerial Triangulation for Fast UAV-based Mapping
CoRR(2024)
摘要
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.
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