ABSTRACT:Historical photographs contain high density of information and are of great importance as sources in humanities research. In addition to the semantic indexing of historical images based on metadata, it is also possible to reconstruct geometric information about the depicted objects or the camera position at the time of the recording by employing photogrammetric methods. The approach presented here is intended to investigate (semi-) automated photogrammetric reconstruction methods for heterogeneous collections of historical (city) photographs and photographic documentation for the use in the humanities, urban research and history sciences. From a photogrammetric point of view, these images are mostly digitized photographs. For a photogrammetric evaluation, therefore, the characteristics of scanned analog images with mostly unknown camera geometry, missing or minimal object information and low radiometric and geometric resolution have to be considered. In addition, these photographs have not been created specifically for documentation purposes and so the focus of these images is often not on the object to be evaluated. The image repositories must therefore be subjected to a preprocessing analysis of their photogrammetric usability. Investigations are carried out on the basis of a repository containing historical images of the Kronentor ('crown gate') of the Dresden Zwinger. The initial step was to assess the quality and condition of available images determining their appropriateness for generating three-dimensional point clouds from historical photos using a structure-from-motion evaluation (SfM). Then, the generated point clouds were assessed by comparing them with current measurement data of the same object.

<p><strong>Abstract.</strong> This contribution shows the generation of a benchmark dataset using historical images. The difficulties when working with historical images are pointed out and structured in three categories. Especially large viewpoint differences, image artifacts and radiometric differences lead to weak matching results with classical feature matching approaches. The necessity of publishing an own benchmark dataset is emphasized when comparing to existing datasets which are partly using synthetic data, well-known orientation or strictly categorized image differences. The presented image dataset consists at the moment of 24 images which are oriented in image triples using the properties of the Trifocal Tensor as a more stable image geometry. In the following, three different feature detectors and descriptors that have already been proven well on historical images (MSER, ORB, RIFT) are evaluated using the new benchmark dataset. Then, several outlier removal methods were applied on the detected features. The tests show that for the entirety of image pairs RIFT performs slightly better than the other two methods. Nonetheless, for some image pairs MSER significantly improves the matching score but even so, historical image pairs are difficult to be matched with the presented methods due to challenging outlier removal. Still, the estimated projective relative orientation could be used in an autocalibration approach to place the images in a metric scene.</p>

This contribution proposes a workflow for a completely automatic orientation of historical terrestrial urban images. Automatic structure from motion (SfM) software packages often fail when applied to historical image pairs due to large radiometric and geometric differences causing challenges with feature extraction and reliable matching. As an innovative initialising step, the proposed method uses the neural network D2‐Net for feature extraction and Lowe’s mutual nearest neighbour matcher. The principal distance for every camera is estimated using vanishing point detection. The results were compared to three state‐of‐the‐art SfM workflows (Agisoft Metashape, Meshroom and COLMAP) with the proposed workflow outperforming the other SfM tools. The resulting camera orientation data are planned to be imported into a web and virtual/augmented reality (VR/AR) application for the purpose of knowledge transfer in cultural heritage.

<p class="VARAbstract">This contribution shows the comparison, investigation, and implementation of different access strategies on multimodal data. The first part of the research is structured as a theoretical part opposing and explaining the terms of conventional access, virtual archival access, and virtual museums while additionally referencing related work. Especially, issues that still persist in repositories like the ambiguity or missing of metadata is pointed out. The second part explains the practical implementation of a workflow from a large image repository to various four-dimensional applications. Mainly, the filtering of images and in the following, the orientation of images is explained. Selection of the relevant images is partly done manually but also with the use of deep convolutional neural networks for image classification. In the following, photogrammetric methods are used for finding the relative orientation between image pairs in a projective frame. For this purpose, an adapted Structure from Motion (SfM) workflow is presented, in which the step of feature detection and matching is replaced by the Radiant-Invariant Feature Transform (RIFT) and Matching On Demand with View Synthesis (MODS). Both methods have been evaluated on a benchmark dataset and performed superior than other approaches. Subsequently, the oriented images are placed interactively and in the future automatically in a 4D browser application showing images, maps, and building models Further usage scenarios are presented in several Virtual Reality (VR) and Augmented Reality (AR) applications. The new representation of the archival data enables spatial and temporal browsing of repositories allowing the research of innovative perspectives and the uncovering of historical details.</p><p>Highlights:</p><ul><li>Strategies for a completely automated workflow from image repositories to four-dimensional (4D) access approaches.</li><li>The orientation of historical images using adapted and evaluated feature matching methods.</li><li>4D access methods for historical images and 3D models using web technologies and Virtual Reality (VR)/Augmented Reality (AR).</li></ul><p> </p>

<p><strong>Abstract.</strong> The interdisciplinary research group on four-dimensional research and communication of urban history (Urban History 4D) aims to investigate and develop methods and technologies to access extensive repositories of historical media and their contextual information in a spatial model, with an additional temporal component. This will make content accessible to different target groups, researchers and the public, via a 4D Browser and an Augmented Reality app for mobile devices. One goal is to improve the accessibility of media repositories and develop suitable solutions for data preparation and information research, making extensive use of visualizations. An interdisciplinary approach is taken to ensure that visualizations for research are comprehensible and meet scientific standards. The investigation of spatial visualizations of image repositories and their visual representation in a coherent context includes frequencies, directions, and perspectives within the image material, which can be grasped through quantitative methods. This paper introduces two main investigations into (1) quantitative data visualization with photography and (2) the plausibility and perception of 3D reconstructions.</p>

ABSTRACT:This contribution shows an approach to match historical images from the photo library of the Saxon State and University Library Dresden (SLUB) in the context of a historical three-dimensional city model of Dresden. In comparison to recent images, historical photography provides diverse factors which make an automatical image analysis (feature detection, feature matching and relative orientation of images) difficult. Due to e. g. film grain, dust particles or the digitalization process, historical images are often covered by noise interfering with the image signal needed for a robust feature matching. The presented approach uses quadrilaterals in image space as these are commonly available in man-made structures and façade images (windows, stones, claddings). It is explained how to generally detect quadrilaterals in images. Consequently, the properties of the quadrilaterals as well as the relationship to neighbouring quadrilaterals are used for the description and matching of feature points. The results show that most of the matches are robust and correct but still small in numbers.

<p><strong>Abstract.</strong> This contribution shows ongoing interdisciplinary research of the project HistStadt4D, concerning the investigation and development of different multimodal access strategies on large image repositories. The first part of the presented research introduces different methods of access, where classical analogue access stands in contrast to digital access strategies such as online collections, Web3D, Augmented Reality (AR) and Virtual Reality (VR). We discuss the main persisting issues of libraries, advantages of digital methods, and different access tools. The second part shows technologies and workflows used to create various access possibilities. The photogrammetric and geo-informational work serves as a technical basis for a 3D WebGIS as well as multiple AR/VR applications, which require spatial oriented images, object coordinates, and further spatial data. We introduce a research environment that allows art historians spatial access to historical photography, integrating 3D/4D models with photographic documents of the respective architecture. For dissemination of research results in installations and museums, we present fully immersive VR as well as handheld AR applications allowing users a free exploration of historical photography in a spatial setting.</p>

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