Fast and Accurate Satellite Multi-view Stereo using Edge-Aware Interpolation

In this paper, we propose a fast and accurate approach for 3D reconstructions from satellite images. Compared with traditional images, satellite imagery features enormous pixel count, inaccurate camera calibration, and low ground sampling rate, all of which makes multi-view stereo for satellite images more challenging. Our approach first computes sparse but reliable 2D feature matches between image pairs. Such feature matches are used to compensate the extrinsic calibration errors. Preliminary dense correspondences are obtained via edge-aware interpolation of sparse feature matches. We rely on fast bilateral smoothing to refine such initial dense matches, which greatly improves the computational efficiency of our method. The smoothed dense correspondences and the refined camera model are then used to obtain dense 3D point clouds via triangulation. Our proposed method outperforms state-of-the-art baseline methods in both efficiency and accuracy on real-world datasets.