Keywords: Photogrammetry, Surface Reconstruction, Multi-View Stereo, Dense Image Matching, Structure from Motion 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.

The advent of technology in digital cameras and their incorporation into virtually any smart mobile device has led to an explosion of the number of photographs taken every day. Today, the number of images stored online and available freely has reached unprecedented levels. It is estimated that in 2011, there were over 100 billion photographs stored in just one of the major social media sites. This number is growing exponentially. Moreover, 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 twodimensional photographs. The existence of powerful and affordable computational machinery not only the reconstruction of complex structures but also entire cities. This paper illustrates an overview of our methodology for producing 3D models of Cultural Heritage structures such as monuments and artefacts from 2D data (pictures, video), available on Internet repositories, social media, Google Maps, Bing, etc. We also present new approaches to semantic enrichment of the end results and their subsequent export to Europeana, the European digital library, for integrated, interactive 3D visualisation within regular web browsers using WebGl and X3D. Our main goal is to enable historians, architects, archaeologists, urban planners and affiliated professionals to reconstruct views of historical structures from millions of images floating around the web and interact with them.

Purpose: Representative cultural heritage sites and monuments around the world have been lost or damaged by natural disasters, human conflicts and daily erosion and deterioration. Documentation and digital preservation by using three-dimensional (3D) modeling techniques enables to ensure the knowledge and access for future generations. Efficient working methods and techniques should be proposed for this purpose. Design/methodology/approach: In this paper, a methodology for the generation of 3D photorealistic models of representative historical buildings is introduced, for using data are obtained using terrestrial laser scanning systems and photogrammetry. Findings: In this paper, an approach to reconstruct 3D photorealistic models by using laser scanning and photogrammetric data is shown. Combination of data from both sources offers an improved solution for 3D reconstruction of historical buildings, sites and places. Integration of 3D models into virtual globes and/or software applications can ensure digital preservation and knowledge for next generations. Research limitations/implications: Results obtained in a concrete building are shown. However, each building or studied area can show some other different drawbacks. Practical implications: The study enables to generate 3D and four-dimensional models of most valuable buildings and contribute to the preservation and documentation of the cultural heritage. Social implications: The study enables digital documentation and preservation of cultural heritage. Originality/value: A proper solution at field (in a real and complicated case) is explained, in addition to the results, which are shown.

Modern high-resolution satellite sensors collect optical imagery with ground sampling distances (GSDs) of 30–50 cm, which has sparked a renewed interest in photogrammetric 3D surface reconstruction from satellite data. State-of-the-art reconstruction methods typically generate 2.5D elevation data. Here, we present an approach to recover full 3D surface meshes from multi-view satellite imagery. The proposed method takes as input a coarse initial mesh and refines it by iteratively updating all vertex positions to maximise the photo-consistency between images. Photo-consistency is measured in image space, by transferring texture from one image to another via the surface. We derive the equations to propagate changes in texture similarity through the rational function model (RFM), often also referred to as rational polynomial coefficient (RPC) model. Furthermore, we devise a hierarchical scheme to optimise the surface with gradient descent. In experiments with two different datasets, we show that the refinement improves the initial digital elevation models (DEMs) generated with conventional dense image matching. Moreover, we demonstrate that our method is able to reconstruct true 3D geometry, such as facade structures, if off-nadir views are available.