Orientation and Dense Reconstruction of Unordered Terrestrial and Aerial Wide Baseline Image Sets

In this paper we present an approach for detailed and precise automatic dense 3D reconstruction using images from consumer cameras. The major difference between our approach and many others is that we focus on wide-baseline image sets. We have combined and improved several methods, particularly, least squares matching, RANSAC, scale-space maxima and bundle adjustment, for robust matching and parameter estimation. Point correspondences and the five-point algorithm lead to relative orientation. Due to our robust matching method it is possible to orient images under much more unfavorable conditions, for instance concerning illumination changes or scale differences, than for often used operators such as SIFT. For dense reconstruction, we use our orientation as input for Semiglobal Matching (SGM) resulting into dense depth images. The latter can be fused into a 2.5D model for eliminating the redundancy of the highly overlapping depth images. However, some applications require full 3D models. A solution to this problem is part of our current work, for which preliminary results are presented in this paper. With very small unmanned aerial systems (Micro UAS) it is possible to acquire images which have a perspective similar to terrestrial images and can thus be combined with them. Such a combination is useful for an almost complete 3D reconstruction of urban scenes. We have applied our approach to several hundred aerial and terrestrial images and have generated detailed 2.5D and 3D models of urban areas.