Oblique View Selection for Efficient and Accurate Building Reconstruction in Rural Areas Using Large-Scale UAV Images

Abstract

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.

Oblique Aerial Images: Geometric Principles, Relationships and Definitions

“…Both the scientific community and multiple companies have been utilizing oblique images for diverse applications, leading to significant advancements in their automated processing [2,3]. Applications utilizing oblique aerial images include-but are not limited to-image matching [4][5][6], georeferencing [3,[7][8][9], orientation and structure from motion procedures [10][11][12][13][14][15], multi-view stereo and 3D modeling pipelines [16][17][18][19][20], texture mapping [21][22][23], object detection [24,25], building identification [26][27][28], semantic segmentation of 3D city models [29] and buildings [30,31], building classification [32], extraction of post-disaster structural damages [33][34][35], historic building information modeling (HBIM) [36], reconstruction of LoD-2 building models [37], cadastral mapping [38], 3D reconstruction of canopy [39] and estimation of canopy height [40], moving car recognition [41], animal detection [42], and river surface ice quantification …”