Abstract
With extensive applications of Unmanned Aircraft Vehicle (UAV) in the field of remote sensing, 3D reconstruction using aerial images has been a vibrant area of research. However, fast large-scale 3D reconstruction is a challenging task. For aerial image datasets, large scale means that the number and resolution of images are enormous, which brings significant computational cost to the 3D reconstruction, especially in the process of Structure from Motion (SfM). In this paper, for fast large-scale SfM, we propose a clustering-aligning framework that hierarchically merges partial structures to reconstruct the full scene. Through image clustering, an overlapping relationship between image subsets is established. With the overlapping relationship, we propose a similarity transformation estimation method based on joint camera poses of common images. Finally, we introduce the closed-loop constraint and propose a similarity transformation-based hybrid optimization method to make the merged complete scene seamless. The advantage of the proposed method is a significant efficiency improvement without a marginal loss in accuracy. Experimental results on the Qinling dataset captured over Qinling mountain covering 57 square kilometers demonstrate the efficiency and robustness of the proposed method.
Highlights
With the development of Unmanned Aircraft Vehicle (UAV), low-altitude remote sensing is playing an increasingly important role in land cover monitoring [1,2,3], heritage site protection [4], and vegetation observation [5,6,7]
Structure from Motion (SfM) is a 3D reconstruction method used to recover the 3D structure of stationary scenes from a set of projective measurements, via motion estimation of the cameras corresponding to images
In the process of aligning partial reconstructions, we present a robust initial similarity transformation estimation method based on joint camera poses of common images across image subsets without 3D point matches
Summary
With the development of Unmanned Aircraft Vehicle (UAV), low-altitude remote sensing is playing an increasingly important role in land cover monitoring [1,2,3], heritage site protection [4], and vegetation observation [5,6,7]. For these applications, aerial imagery-based 3D reconstruction of large-scale scenes is highly desired. Structure from Motion (SfM) is a 3D reconstruction method used to recover the 3D structure of stationary scenes from a set of projective measurements, via motion estimation of the cameras corresponding to images. SfM is the primary task for the research and application of 3D reconstruction using aerial images
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