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 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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ABS TRACT:The easy generation of 3D geometries (point clouds or polygonal models) with fully automated image-based methods poses nontrivial problems on how to check a posteriori the quality of the achieved results. Clear statements and procedures on how to plan the camera network, execute the survey and use automatic tools to achieve the prefixed requirements are still an open issue. Although such issues had been discussed and solved some years ago, the importance of camera network geometry is today often underestimated or neglected in the cultural heritage field. In this paper different camera network geometries, with normal and convergent images, are analyzed and the accuracy of the produced results are compared to ground truth measurements.