Generic object density estimation and counting plays an important role in various remote sensing applications. Therefore, this work discusses available Earth observation based datasets for both tasks. Critical aspects of the current evaluation protocol are identified and analyzed, which yields novel design issues for an optimal future protocol. Moreover, the parameters for an ideal benchmark dataset are defined which allows appropriate evaluation and ranking of methodologies.

In this paper we propose a new method to incrementally extract a surface from a consecutively growing Structure-from-Motion (SfM) point cloud in real-time. Our method is based on a Delaunay triangulation (DT) on the 3D points. The core idea is to robustly label all tetrahedra into freeand occupied space using a random field formulation and to extract the surface as the interface between differently labeled tetrahedra. For this reason, we propose a new energy function that achieves the same accuracy as state-of-the-art methods but reduces the computational effort significantly. Furthermore, our new formulation allows us to extract the surface in an incremental manner, i. e. whenever the point cloud is updated we adapt our energy function. Instead of minimizing the updated energy with a standard graph cut, we employ the dynamic graph cut of Kohli et al. [1] which enables efficient minimization of a series of similar random fields by re-using the previous solution. In such a way we are able to extract the surface from an increasingly growing point cloud nearly independent of the overall scene size. Energy Function for Surface Extraction Our method formulates surface extraction as a binary labeling problem, with the goal of assigning each tetrahedron either a free or occupied label. For this reason, we model the probabilities that a tetrahedron is free- or occupied space analyzing the set of rays that connect all 3D points to image features. Following the idea of the truncated signed distance function (TSDF), which is known from voxel-based surface reconstructions, a tetrahedron in front of a 3D point X has a high probability to be free space, whereas a tetrahedron behind X is presumably occupied space. We further assume that it is very unlikely that neighboring tetrahedra obtain different labels, except for pairs of tetrahedra that have a ray through the face connecting both. Such a labeling problem can be elegantly formulated as a pairwise random field and since our priors are submodular, we can efficiently find a global optimal labeling solution e. g. using graph cuts. In contrast to existing methods like [2], our energy depends only on the visibility information that is directly connected to the four 3D points that span the tetrahedraVi. Hence a modification of the tetrahedral structure by inserting new points has only limited effect on the energy function. This property enables us to easily adopt the energy function to a modified tetrahedral structure. Incremental Surface Extraction To enable efficient incremental surface reconstruction, our method has to consecutively integrate new scene information (3D points as well as visibility information) in the energy function and to minimize the modified energy efficiently. Integrating new visibility information, i. e. adding rays for newly available 3D points, affects only those terms of the energy function that relate

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.

Self-localization in large environments is a vital task for accurately registered information visualization in outdoor Augmented Reality (AR) applications. In this work, we present a system for self-localization on mobile phones using a GPS prior and an online-generated panoramic view of the user's environment. The approach is suitable for executing entirely on current generation mobile devices, such as smartphones. Parallel execution of online incremental panorama generation and accurate 6DOF pose estimation using 3D point reconstructions allows for real-time self-localization and registration in large-scale environments. The power of our approach is demonstrated in several experimental evaluations.

Repetitive and ambiguous visual structures in general pose a severe problem in many computer vision applications. Identification of incorrect geometric relations between images solely based on low level features is not always possible, and a more global reasoning approach about the consistency of the estimated relations is required. We propose to utilize the typically observed redundancy in the hypothesized relations for such reasoning, and focus on the graph structure induced by those relations. Chaining the (reversible) transformations over cycles in this graph allows to build suitable statistics for identifying inconsistent loops in the graph. This data provides indirect evidence for conflicting visual relations. Inferring the set of likely false positive geometric relations from these non-local observations is formulated in a Bayesian framework. We demonstrate the utility of the proposed method in several applications, most prominently the computation of structure and motion from images.

Localization of mobile devices is an essential task in Augmented Reality and has therefore been an active research topic for many years. Typically, indoor tracking approaches, such as methods based on infrared or ultra-wide-band, require preparations of the environment and special hardware sensors. Conversely, image feature tracking approaches can provide orientation estimates without special tracking hardware installations. With the advent of mobile devices equipped with sensors such as digital cameras, image based localization gains importance in Augmented Reality. Typically, fiducial marker tracking was considered as a standard image based localization method. We propose the use of natural image feature based tracking methods, which are a generalization of the same principals but do not require the presence of fiducial tracking targets.

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.

We present an approach towards automatic reconstruction of large assemblies of fiducial markers scattered throughout a wide indoor area, using a computer vision based reconstruction approach. The data is acquired from a video stream captured with a monoscopic camera. The system is capable of creating markers models that are significantly larger in physical area and number of markers than with previous approaches.