Automatic 3D Reconstruction From Multi-Date Satellite Images

Gabriele Facciolo, Carlo de Franchis, Enric Meinhardt-Llopis; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 57-66

We propose an algorithm for computing a 3D model from several satellite images of the same site. The method works even if the images were taken at different dates with important lighting and vegetation differences. We show that with a large number of input images the resulting 3D models can be as accurate as those obtained from a single same-date stereo pair. To deal with seasonal vegetation changes, we propose a strategy that accounts for the multi-modal nature of 3D models computed from multi-date images. Our method uses a local affine camera approximation and thus focuses on the 3D reconstruction of small areas. This is a common setup in urgent cartography for emergency management, for which abundant multi-date imagery can be immediately available to build a reference 3D model. A preliminary implementation of this method was used to win the IARPA Multi-View Stereo 3D Mapping Challenge 2016. Experiments on the challenge dataset are used to substantiate our claims.