No abstract

Image matching has a history of more than 50 years, with the first experiments performed with analogue procedures for cartographic and mapping purposes. The recent integration of computer vision algorithms and photogrammetric methods is leading to interesting procedures which have increasingly automated the entire image-based 3D modelling process. Image matching is one of the key steps in 3D modelling and mapping. This paper presents a critical review and analysis of four dense image-matching algorithms, available as open-source and commercial software, for the generation of dense point clouds. The eight datasets employed include scenes recorded from terrestrial and aerial blocks, acquired with convergent and normal (parallel axes) images, and with different scales. Geometric analyses are reported in which the point clouds produced with each of the different algorithms are compared with one another and also to groundtruth data.It is quite evident that even with our past progress, we have only scratched the surface of the possibilities in the use of photogrammetry. ) at different scales. Complex scenes and objects can be surveyed and reconstructed using a large set of images with very satisfactory results (Fig. 1). In particular, methods for dense point-cloud generation (dense image matching) are increasingly available for professional and amateur applications such as 3D modelling and mapping, robotics, medical imaging, surveillance, tracking and navigation.Due to the availability of a number of different low-cost and open-source software systems, automated 3D reconstruction methods are becoming very popular. Nevertheless, the metrological and reliability aspects of the resulting 3D measurements and modelling should not be ignored, particularly if the community wishes to adopt such solutions not only for quick 3D modelling and visualisation but also for accurate measurement purposes. To this end, clear accuracy statements, benchmarking and evaluations must be carried out.This paper presents a critical review and analysis of selected dense image-matching algorithms. The algorithms considered are from both the commercial and open-source domains. The datasets adopted for the testing (Table I and Fig. 3) include terrestrial and aerial image blocks, acquired with convergent and normal (parallel axes) images at different scales and resolution. With respect to other reported benchmarking datasets, the imagery considered here is of higher resolution and it covers more complex scenes. Moreover, the evaluations presented are performed on the raw output of the matching (that is, on the point cloud) and not at the mesh level. The algorithms are evaluated according to their ability to produce dense and high-quality 3D point clouds, as well as according to computation time. Geometric analyses are reported, in which the point clouds produced with each of the different algorithms are compared with one another and also to ground-truth data. Laser Scanning or Photogrammetry?Since 2000, range sensors, both airborne and terrestrial, ...

UAV platforms are nowadays a valuable source of data for inspection, surveillance, mapping and 3D modeling issues. New applications in the short-and close-range domain are introduced, being the UAVs a low-cost alternatives to the classical manned aerial photogrammetry. Rotary or fixed wing UAVs, capable of performing the photogrammetric data acquisition with amateur or SLR digital cameras, can fly in manual, semi-automated and autonomous modes. With a typical photogrammetric pipeline, 3D results like DSM/DTM, contour lines, textured 3D models, vector data, etc. can be produced, in a reasonable automated way. The paper reports the latest developments of UAV image processing methods for photogrammetric applications, mapping and 3D modeling issues. Automation is nowadays necessary and feasible at the image orientation, DSM generation and orthophoto production stages, while accurate feature extraction is still an interactive procedure. New perspectives are also addressed. Figure 1: Example of scenes surveyed with a UAV system (Microdrone MD4-200) and photogrammetric results achieved from the acquired images: digital surface model, orthoimages and overlaid contours (archaeological area in Montalcino, Italy).

UAVs-unmanned aerial vehicles-facilitate data acquisition at temporal and spatial scales that still remain unachievable for traditional remote sensing platforms. However, current legal frameworks that regulate UAVs present significant barriers to research and development. To highlight the importance, impact, and diversity of UAV regulations, this paper provides an exploratory investigation of UAV regulations on the global scale. For this, the methodological approach consists of a research synthesis of UAV regulations, including a thorough literature review and a comparative analysis of national regulatory frameworks. Similarities and contrasting elements in the various national UAV regulations are explored including their statuses from the perspectives of past, present, and future trends. Since the early 2000s, countries have gradually established national legal frameworks. Although all UAV regulations have one common goal-minimizing the risks to other airspace users and to both people and property on the ground-the results reveal distinct variations in all the compared variables. Furthermore, besides the clear presence of legal frameworks, market forces such as industry design standards and reliable information about UAVs as public goods are expected to shape future developments.

No abstract