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In this paper the main problems and the available solutions are addressed for the generation of 3D models from terrestrial images. Close range photogrammetry has dealt for many years with manual or automatic image measurements for precise 3D modelling. Nowadays 3D scanners are also becoming a standard source for input data in many application areas, but image-based modelling still remains the most complete, economical, portable, flexible and widely used approach. In this paper the full pipeline is presented for 3D modelling from terrestrial image data, considering the different approaches and analysing all the steps involved. IntroductionThree-dimensional (3D) modelling of an object can be seen as the complete process that starts from data acquisition and ends with a 3D virtual model visually interactive on a computer. Often 3D modelling is meant only as the process of converting a measured point cloud into a triangulated network (''mesh'') or textured surface, while it should describe a more complete and general process of object reconstruction. Three-dimensional modelling of objects and scenes is an intensive and long-lasting research problem in the graphic, vision and photogrammetric communities. Three-dimensional digital models are required in many applications such as inspection, navigation, object identification, visualisation and animation. Recently it has become a very important and fundamental step in particular for cultural heritage digital archiving. The motivations are different: documentation in case of loss or damage, virtual tourism and museum, education resources, interaction without risk of damage, and so forth. The requirements specified for many applications, including digital archiving and mapping, involve high geometric accuracy, photo-realism of the results and the modelling of the complete details, as well as the automation, low cost, portability and flexibility of the modelling technique. Therefore, selecting the most appropriate 3D modelling technique to satisfy all requirements for a given application is not always an easy task.Digital models are nowadays present everywhere, their use and diffusion are becoming very popular through the Internet and they can be displayed on low-cost computers. Although it seems easy to create a simple 3D model, the generation of a precise and photo-realistic computer model of a complex object still requires considerable effort.The most general classification of 3D object measurement and reconstruction techniques can be divided into contact methods (for example, using coordinate measuring machines,

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, ...

Abstract:The importance of landscape and heritage recording and documentation with optical remote sensing sensors is well recognized at international level. The continuous development of new sensors, data capture methodologies and multi-resolution 3D representations, contributes significantly to the digital 3D documentation, mapping, conservation and representation of landscapes and heritages and to the growth of research in this field. This article reviews the actual optical 3D measurement sensors and 3D modeling techniques, with their limitations and potentialities, requirements and specifications. Examples of 3D surveying and modeling of heritage sites and objects are also shown throughout the paper.

Unmanned Aerial Vehicles (UAV)-based remote sensing offers great possibilities to acquire in a fast and easy way field data for precision agriculture applications. This field of study is rapidly increasing due to the benefits and advantages for farm resources management, particularly for studying crop health. This paper reports some experiences related to the analysis of cultivations (vineyards and tomatoes) with Tetracam multispectral data. The Tetracam camera was mounted on a multi-rotor hexacopter. The multispectral data were processed with a photogrammetric pipeline to create triband orthoimages of the surveyed sites. Those orthoimages were employed to extract some Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Soil Adjusted Vegetation Index (SAVI), examining the vegetation vigor for each crop. The paper demonstrates the great potential of high-resolution UAV data and photogrammetric techniques applied in the agriculture framework to collect multispectral images and OPEN ACCESS Remote Sens. 2015, 7 4027 evaluate different VI, suggesting that these instruments represent a fast, reliable, and cost-effective resource in crop assessment for precision farming applications.

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).

Abstract. The recent developments in automated image processing for 3D reconstruction purposes have led to the diffusion of low-cost and open-source solutions which can be nowadays used by everyone to produce 3D models. The level of automation is so high that many solutions are black-boxes with poor repeatability and low reliability. The article presents an investigation of automated image orientation packages in order to clarify potentialities and performances when dealing with large and complex datasets. Introduction3D model generation of artifacts, monuments or large environments is becoming a common practice for applications like documentation, digital restoration, visualization, inspection, planning, AR/VR, gaming, entertainment, etc. 3D modeling should be intended as the entire procedure which produces a three-dimensional product starting from surveyed data (reality-based approach) or other sources of information. Data can be recorded with digital cameras or active sensors leading to the well-known image-based [1] or range-based [2] approaches, respectively. The image-based approach is generally considered a low-cost method (in particular for terrestrial applications), flexible, portable and capable of reconstructing lost scenarios simply using archives images [3]. In the recent months different solutions have become available for the automated processing of images and the derivation of 3D information and models. The processing mainly includes image orientation and dense 3D reconstruction with an incredible level of automation. The article investigates the performances and reliability of some low-cost commercial and open-source packages able to automatically process large blocks of images and retrieve the unknown camera poses. Different datasets are used comparing the software outcomes in terms of visual and metric analyses.

In this paper an automated methodology is presented (i) to orient a set of close‐range images captured with a calibrated camera, and (ii) to extract dense and accurate point clouds starting from the estimated orientation parameters. The whole procedure combines different algorithms and techniques in order to obtain accurate 3D reconstructions in an automatic way. The exterior orientation parameters are estimated using a photogrammetric bundle adjustment with the image correspondences detected using area‐ and feature‐based matching algorithms. Surface measurements are then performed using advanced multi‐image matching techniques based on multiple image primitives. To demonstrate the reliability, precision and robustness of the procedure, several tests on different kinds of free‐form objects are illustrated and discussed in the paper. Three‐dimensional comparisons with range‐based data are also carried out.