We present an image-based 3D reconstruction pipeline for acquiring geo-referenced semi-dense 3D models. Multiple overlapping images captured from a micro aerial vehicle platform provide a highly redundant source for multi-view reconstructions. Publicly available geo-spatial information sources are used to obtain an approximation to a digital surface model (DSM). Models obtained by the semi-dense reconstruction are automatically aligned to the DSM to allow the integration of highly detailed models into the original DSM and to provide geographic context.

Summary form only given. We report on a disruption in organizational dynamics arising from the introduction of model-driven development tools in General Motors. The introduction altered the balance of collaboration deeply, and the organization is still negotiating with its aftermath. Our report illustrates one consequence of tool adoption in groups, and that these consequences should be understood to facilitate technical change.

Highly accurate localization of a micro aerial vehicle (MAV) with respect to a scene is important for a wide range of applications, in particular surveillance and inspection. Most existing approaches to visual localization focus on indoor environments, while such tasks require outdoor navigation. Within this work, we introduce a novel algorithm for monocular visual localization for MAVs based on the concept of virtual views in 3D space. Under the assumption that significant parts of the scene do not alter their geometry and serve as natural landmarks, the accuracy of our visual approach outperforms consumer grade GPS systems. In an experimental setup we compare our approach to a state-of-the-art visual SLAM algorithm and evaluate the performance by geometric validation from an observer's view. As our method directly allows global registration, it is neither prone to drift nor bias. This makes it well suited for long-term autonomous navigation.

Efficient view registration with respect to a given 3D reconstruction has many applications like inside-out tracking in indoor and outdoor environments, and geo-locating images from large photo collections. We present a fast location recognition technique based on structure from motion point clouds. Vocabulary tree-based indexing of features directly returns relevant fragments of 3D models instead of documents from the images database. Additionally, we propose a compressed 3D scene representation which improves recognition rates while simultaneously reducing the computation time and the memory consumption. The design of our method is based on algorithms that efficiently utilize modern graphics processing units to deliver real-time performance for view registration. We demonstrate the approach by matching hand-held outdoor videos to known 3D urban models, and by registering images from online photo collections to the corresponding landmarks.

We present a fast and memory efficient method for localizing a mobile user's 6DOF pose from a single camera image. Our approach registers a view with respect to a sparse 3D point reconstruction. The 3D point dataset is partitioned into pieces based on visibility constraints and occlusion culling, making it scalable and efficient to handle. Starting with a coarse guess, our system only considers features that can be seen from the user's position. Our method is resource efficient, usually requiring only a few megabytes of memory, thereby making it feasible to run on low-end devices such as mobile phones. At the same time it is fast enough to give instant results on this device class.

Practically all existing approaches to structure and motion computation use only positive image correspondences to verify the camera pose hypotheses. Incorrect epipolar geometries are solely detected by identifying outliers among the found correspondences. Ambiguous patterns in the images are often incorrectly handled by these standard methods. In this work we propose two approaches to overcome such problems. First, we apply non-monotone reasoning on view triplets using a Bayesian formulation. In contrast to two-view epipolar geometry, image triplets allow the prediction of features in the third image. Absence of these features (i.e. missing correspondences) enables additional inference about the view triplet. Furthermore, we integrate these view triplet handling into an incremental procedure for structure and motion computation. Thus, our approach is able to refine the maintained 3D structure when additional image data is provided.

This work reports on the advances and on the current status of a terrestrial city modeling approach, which uses images contributed by end-users as input. Hence, the Wiki principle well known from textual knowledge databases is transferred to the goal of incrementally building a virtual representation of the occupied habitat. In order to achieve this objective, many state-of-the-art computer vision methods must be applied and modified according to this task. We describe the utilized 3D vision methods and show initial results obtained from the current image database acquired by in-house participants.