Efficient Multi-View 3D Dense Matching for Large-Scale Aerial Images Using a Divide-and-Conquer Scheme
Junshi Xue, Xiangning Chen, Hui Jun Yi · 2018
This paper proposes a novel multi-view 3D dense matching method for large-scale aerial images using a divide-and-conquer scheme. Firstly, the original sparse reconstruction result is divided into several sub-clusters based on the relationship of the camera projection location, and the bounding box of each sub-cluster is obtained. An efficient patch-based stereo matching strategy is then performed, followed by multi-photo geometrically constrained (MPGC) matching optimization to generate a depth map for each image in the sub-clusters, with the limited patch expansion range according to the bounding box of the sub-cluster. Redundancy points are removed by enhanced depth consistency in different views, which contributes to high-accuracy depth map fusion. Lastly, the dense points of each sub-cluster can be easily grouped together due to the determined boundaries. This method can be easily parallelized at the image level, and is highly suitable for the large-scale reconstruction of aerial images. The experimental results show that the proposed method has advantages over the state-of-the-art method in terms of reconstruction accuracy and efficiency.