Recent progress in computer vision has been dominated by deep neural networks trained over larges amount of labeled data. Collecting such datasets is however a tedious, often impossible task; hence a surge in approaches relying solely on synthetic data for their training. For depth images however, discrepancies with real scans still noticeably affect the end performance. We thus propose an end-to-end framework which simulates the whole mechanism of these devices, generating realistic depth data from 3D models by comprehensively modeling vital factors e.g. sensor noise, material reflectance, surface geometry. Not only does our solution cover a wider range of sensors and achieve more realistic results than previous methods, assessed through extended evaluation, but we go further by measuring the impact on the training of neural networks for various recognition tasks; demonstrating how our pipeline seamlessly integrates such architectures and consistently enhances their performance.

Augmented reality (AR) allows for an on-site presentation of information that is registered to the physical environment. Applications from civil engineering, which require users to process complex information, are among those which can benefit particularly highly from such a presentation. In this paper, we will describe how to use AR to support monitoring and documentation of construction site progress. For these tasks, the responsible staff usually requires fast and comprehensible access to progress information to enable comparison to the as-built status as well as to as-planned data. Instead of tediously searching and mapping related information to the actual construction site environment, our AR system allows for the access of information right where it is needed. This is achieved by superimposing progress as well as as-planned information onto the user's view of the physical environment. For this purpose, we present an approach that uses aerial 3-D reconstruction to automatically capture progress information and a mobile AR client for on-site visualization. Within this paper, we will describe in greater detail how to capture 3-D, how to register the AR system within the physical outdoor environment, how to visualize progress information in a comprehensible way in an AR overlay, and how to interact with this kind of information. By implementing such an AR system, we are able to provide an overview about the possibilities and future applications of AR in the construction industry.

The quality and completeness of 3D models obtained by Structure-fromMotion (SfM) heavily depend on the image acquisition process. If the user gets feedback about the reconstruction quality already during the acquisition, he can optimize this process. The goal of this paper is to support a user during image acquisition by giving online feedback of the current reconstruction quality. We propose an online SfM method that integrates wide-baseline still-images in an online fashion into a consistent reconstruction and we derive a surface model given the SfM point cloud. To guide the user to scene parts that are captured not very well, we colour the mesh according to redundancy and resolution information. In the experiments, we show that our approach makes the final SfM result predictable already during image acquisition. The method is suited for large-scale reconstructions as obtained by flying micro aerial vehicles as well as on small indoor environments. We propose a method that supports a user in the acquisition process in two ways: (a) sparse online SfM with accuracy close to offline methods and (b) surface extraction and quality visualization. The workflow of our method is shown in Figure 1.

In this paper we present an approach for visualizing time-oriented data of dynamic scenes in an on-site AR view. Visualizations of time-oriented data have special challenges compared to the visualization of arbitrary virtual objects. Usually, the 4D data occludes a large part of the real scene. Additionally, the data sets from different points in time may occlude each other. Thus, it is important to design adequate visualization techniques that provide a comprehensible visualization. In this paper we introduce a visualization concept that uses overview and detail techniques to present 4D data in different detail levels. These levels provide at first an overview of the 4D scene, at second information about the 4D change of a single object and at third detailed information about object appearance and geometry for specific points in time. Combining the three levels of detail with interactive transitions such as magic lenses or distorted viewing techniques enables the user to understand the relationship between them. Finally we show how to apply this concept for construction site documentation and monitoring.

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.

Covariance matrices have recently been a popular choice for versatile tasks like recognition and tracking due to their powerful properties as local descriptor and their low computational demands. This paper outlines similarities of covariance matrices to the well-known structure tensor. We show that the generalized version of the structure tensor is a powerful descriptor and that it can be calculated in constant time by exploiting the properties of integral images. To measure the similarities between several structure tensors, we describe an approximation scheme which allows comparison in a Euclidean space. Such an approach is also much more efficient than the common, computationally demanding Riemannian Manifold distances. Experimental evaluation proves the applicability for the task of object tracking demonstrating improved performance compared to covariance tracking.

This paper proposes an efficient approach for semantic image classification by integrating additional contextual constraints such as class co-occurrences into a randomized forest classification framework. The randomized forest classifier performs an initial yet local classification on the pixel level by using powerful covariance matrix based descriptors as feature representation. Furthermore, we exploit multiple unsupervised image partitions to provide a reliable spatial region support and to capture the real object boundaries. An information theoretic driven approach detects consistently classified regions and generates a representative segmentation incorporating the classification result on the pixel level. Moreover, we use a conditional random field formulation to obtain a final labeling including context information individually generated for each test image. To illustrate state-of-the-art performance, we run experiments on the two versions of the MSRC [21] dataset with 9 and 21 object classes and on the PASCAL VOC2007 [5] image collection.

The automatic creation of 3D models of urban spaces has become a very active field of research. This has been inspired by recent applications in the location-awareness on the Internet, as demonstrated in maps.live.com and similar websites. The level of automation in creating 3D city models has increased considerably, and has benefited from an increase in the redundancy of the source imagery, namely digital aerial photography. In this paper we argue that the next big step forward is to replace photographic texture by an interpretation of what the texture describes, and to achieve this fully automatically. One calls the result "semantic knowledge". For example we want to know that a certain part of the image is a car, a person, a building, a tree, a shrub, a window, a door, instead of just a collection of 3D points or triangles with a superimposed photographic texture. We investigate object recognition methods to make this next big step. We demonstrate an early result of using the on-line variant of a Boosting algorithm to indeed detect cars in aerial digital imagery to a satisfactory and useful level of completeness. And we show that we can use this semantic knowledge to produce improved orthophotos. We expect that also the 3D models will be improved by the knowledge of cars.

This paper demonstrates how to reduce the hand labeling effort considerably by 3D information in an object detection task. In particular, we demonstrate how an efficient car detector for aerial images with minimal hand labeling effort can be build. We use an on-line boosting algorithm to incrementally improve the detection results. Initially, we train the classifier with a single positive (car) example, randomly drawn from a fixed number of given samples. When applying this detector to an image we obtain many false positive detections. We use information from a stereo matcher to detect some of these false positives (e.g. detected cars on a facade) and feed back this information to the classifier as negative updates. This improves the detector considerably, thus reducing the number of false positives. We show that we obtain similar results to hand labeling by iteratively applying this strategy. The performance of our algorithm is demonstrated on digital aerial images of urban environments.