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.
Change detection is one of the most important tasks when unmanned aerial vehicles (UAV) are used for video reconnaissance and surveillance. In this paper, we address changes on short time scale, i.e. the observations are taken within time distances of a few hours. Each observation is a short video sequence corresponding to the near-nadir overflight of the UAV above the interesting area and the relevant changes are e.g. recently added or removed objects. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are versatile objects like trees and compression or transmission artifacts. To enable the usage of an automatic change detection within an interactive workflow of an UAV video exploitation system, an evaluation and assessment procedure has to be performed. Large video data sets which contain many relevant objects with varying scene background and altering influence parameters (e.g. image quality, sensor and flight parameters) including image metadata and ground truth data are necessary for a comprehensive evaluation. Since the acquisition of real video data is limited by cost and time constraints, from our point of view, the generation of synthetic data by simulation tools has to be considered. In this paper the processing chain of Saur et al. (2014) [1] and the interactive workflow for video change detection is described. We have selected the commercial simulation environment Virtual Battle Space 3 (VBS3) to generate synthetic data. For an experimental setup, an example scenario "road monitoring" has been defined and several video clips have been produced with varying flight and sensor parameters and varying objects in the scene. Image registration and change mask extraction, both components of the processing chain, are applied to corresponding frames of different video clips. For the selected examples, the images could be registered, the modelled changes could be extracted and the artifacts of the image rendering considered as noise (slight differences of heading angles, disparity of vegetation, 3D parallax) could be suppressed. We conclude that these image data could be considered to be realistic enough to serve as evaluation data for the selected processing components. Future work will extend the evaluation to other influence parameters and may include the human operator for mission planning and sensor control
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