Abstract
Large-scale 3D reconstruction for aerial photography is achallenging. For aerial image dataset, large scale means that the amount and resolution of images are enormous, which brings a huge amount of computation in Structure from Motion (SfM) pipeline, especially on the process of feature detection, feature matching and bundle adjustment (BA). In this paper, we present a novel method to solve the large-scale 3D reconstruction in parallel to accelerate the process. It could be generalized as the process of Divide-Reconstruct-Optimize-Fuse. We propose an effective graph-based framework that could robustly conduct aerial images grouping task and optimize parameters to fuse sub-models seamless. Experimental results on large-scale aerial datasets demonstrate the efficiency and robustness of the proposed method.
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