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ABSTRACT:Dense image matching methods enable efficient 3D data acquisition. Digital cameras are available at high resolution, high geometric and radiometric quality and high image repetition rate. They can be used to acquire imagery for photogrammetric purposes in short time. Photogrammetric image processing methods deliver 3D information. For example, Structure from Motion reconstruction methods can be used to derive orientations and sparse surface information. In order to retrieve complete surfaces with high precision, dense image matching methods can be applied. However, a key challenge is the selection of images, since the image network geometry directly impacts the accuracy, as well as the completeness of the point cloud. Thus, the image stations and the image scale have to be selected according carefully to the accuracy requirements. Furthermore, most dense image matching solutions are based on multi-view stereo algorithms, where the matching is performed between selected pairs of images. Thus, stereo models have to be selected from the available dataset in respect to geometric conditions, which influence completeness, precision and processing time. Within the paper, the selection of images and the selection of optimal stereo models are discussed according to to photogrammetric surface acquisition using dense image matching. For this purpose, impacts of the acquisition geometry are evaluated for several datasets. Based on the results, a guideline for the acquisition of imagery for photogrammetric surface acquisition is presented. The simple and efficient capturing approach with "One panorama each step" ensures complete coverage and sufficiently redundant observations for a surface reconstruction with high precision and reliability.

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ABSTRACT:The paper presents a grammar-based approach for the robust automatic reconstruction of 3D interiors from raw point clouds. The core of the approach is a 3D indoor grammar which is an extension of our previously published grammar concept for the modeling of 2D floor plans. The grammar allows for the modeling of buildings whose horizontal, continuous floors are traversed by hallways providing access to the rooms as it is the case for most office buildings or public buildings like schools, hospitals or hotels. The grammar is designed in such way that it can be embedded in an iterative automatic learning process providing a seamless transition from LOD3 to LOD4 building models. Starting from an initial low-level grammar, automatically derived from the window representations of an available LOD3 building model, hypotheses about indoor geometries can be generated. The hypothesized indoor geometries are checked against observation data -here 3D point clouds -collected in the interior of the building. The verified and accepted geometries form the basis for an automatic update of the initial grammar. By this, the knowledge content of the initial grammar is enriched, leading to a grammar with increased quality. This higher-level grammar can then be applied to predict realistic geometries to building parts where only sparse observation data are available. Thus, our approach allows for the robust generation of complete 3D indoor models whose quality can be improved continuously as soon as new observation data are fed into the grammar-based reconstruction process. The feasibility of our approach is demonstrated based on a real-world example.

While location-based services are already well established in outdoor scenarios, they are still not available in indoor environments. The reason for this can be found in two open problems: First, there is still no off-the-shelf indoor positioning system for mobile devices and, second, indoor maps are not publicly available for most buildings. While there is an extensive body of work on the first problem, the efficient creation of indoor maps remains an open challenge. We tackle the indoor mapping challenge in our MapGENIE approach that automatically derives indoor maps from traces collected by pedestrians moving around in a building. Since the trace data is collected in the background from the pedestrians' mobile devices, MapGENIE avoids the labor-intensive task of traditional indoor map creation and increases the efficiency of indoor mapping. To enhance the map building process, MapGE-NIE leverages exterior information about the building and uses grammars to encode structural information about the building. Hence, in contrast to existing work, our approach works without any user interaction and only needs a small amount of traces to derive the indoor map of a building. To demonstrate the performance of MapGENIE, we implemented our system using Android and a foot-mounted IMU to collect traces from volunteers. We show that using our grammar approach, compared to a purely trace-based approach we can identify up to four times as many rooms in a building while at the same time achieving a consistently lower error in the size of detected rooms.

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The number of digital images that are available online today has reached unprecedented levels. Recent statistics showed that by the end of 2013 there were over 250 billion photographs stored in just one of the major social media sites, with a daily average upload of 300 million photos. These photos, apart from documenting personal lives, often relate to experiences in well-known places of cultural interest, throughout several periods of time. Thus from the viewpoint of Cultural Heritage professionals, they constitute valuable and freely available digital cultural content. Advances in the fields of Photogrammetry and Computer Vision have led to significant breakthroughs such as the Structure from Motion algorithm which creates 3D models of objects using their 2D photographs. The existence of powerful and affordable computational machinery enables the reconstruction not only of single structures such as artefacts, but also of entire cities. This paper presents an overview of our methodology for producing cost-effective 4D – i.e. in space and time – models of Cultural Heritage structures such as monuments and artefacts from 2D data (pictures, video) and semantic information, freely available ‘in the wild’, i.e. in Internet repositories and social media. State-of-the-art methods from Computer Vision, Photogrammetry, 3D Reconstruction and Semantic representation are incorporated in an innovative workflow with the main goal to enable historians, architects, archaeologists, urban planners and other cultural heritage professionals to reconstruct cost-effective views of historical structures out of the billions of free images floating around the web and subsequently interact with those reconstructions.