Image-based 3D modeling has been widely used in many areas. Structure from motion is the key to image-based reconstruction. However, the rapid growth of data poses challenges to current SfM solutions. A hierarchical SfM reconstruction methodology for large-scale oblique images is proposed. Firstly, match pairs are selected using positioning and orientation (POS) data and the terrain of the survey area. Then, images are divided to image groups by traversing the selected match pairs. After pairwise image matching, tracks are decimated using an adaptive track selection method. Thirdly, submaps are reconstructed from the image groups in parallel based on incremental SfM in the object space. A novel method based on statistics of the positional difference between common tracks is proposed to detect the outliers in submap merging. Finally, the reconstructed submaps are incrementally merged and optimized. The proposed methodology was used on a large oblique image set. The proposed methodology was compared with the state-of-the-art image-based reconstruction systems COLMAP and Metashape for SfM reconstruction. Experimental results show that the proposed methodology achieved the highest accuracy on the experimental dataset, i.e., about 22.37, and 3.52 times faster than COLMAP and Metashape, respectively. The experimental results demonstrate that the proposed hierarchical SfM methodology is accurate and efficient for large-scale oblique images.
3D building models are widely used in many applications. The traditional image-based 3D reconstruction pipeline without using semantic information is inefficient for building reconstruction in rural areas. An oblique view selection methodology for efficient and accurate building reconstruction in rural areas is proposed in this paper. A Mask R-CNN model is trained using satellite datasets and used to detect building instances in nadir UAV images. Then, the detected building instances and UAV images are directly georeferenced. The georeferenced building instances are used to select oblique images that cover buildings by using nearest neighbours search. Finally, precise match pairs are generated from the selected oblique images and nadir images using their georeferenced principal points. The proposed methodology is tested on a dataset containing 9775 UAV images. A total of 4441 oblique images covering 99.4% of all the buildings in the survey area are automatically selected. Experimental results show that the average precision and recall of the oblique view selection are 0.90 and 0.88, respectively. The percentage of robustly matched oblique-oblique and oblique-nadir image pairs are above 94% and 84.0%, respectively. The proposed methodology is evaluated for sparse and dense reconstruction. Experimental results show that the sparse reconstruction based on the proposed methodology reduces 68.9% of the data processing time, and it is comparably accurate and complete. Experimental results also show high consistency between the dense point clouds of buildings reconstructed by the traditional pipeline and the pipeline based on the proposed methodology.
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