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:Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.

This article reports on a multi-resolution and multi-sensor approach developed for the accurate and detailed 3D modeling of the entire Roman Forum in Pompei, Italy.The archaeological area, approximately 150 × 80 m, contains more than 350 finds spread all over the forum as well as larger mural structures of previous buildings and temples.The interdisciplinary 3D modeling work consists of a multi-scale image-and range-based digital documentation method developed to fulfill all the surveying and archaeological needs and exploit all the intrinsic potentialities of the actual 3D modeling techniques.The data resolution spans from a few decimeters down to few millimeters.The employed surveying methodologies have pros and cons which will be addressed and discussed.The results of the integration of the different 3D data in seamlessly textured 3D model are finally presented and discussed. 41A Multi-Resolution Methodology for the 3D Modeling of Large and Complex Archeological Areas

ABSTRACT:The paper reports some comparisons between commercial software able to automatically process image datasets for 3D reconstruction purposes. The main aspects investigated in the work are the capability to correctly orient large sets of image of complex environments, the metric quality of the results, replicability and redundancy. Different datasets are employed, each one featuring a diverse number of images, GSDs at cm and mm resolutions, and ground truth information to perform statistical analyses of the 3D results. A summary of (photogrammetric) terms is also provided, in order to provide rigorous terms of reference for comparisons and critical analyses.

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