Abstract
BackgroundIn the last decade Photogrammetry has shown to be a valid alternative to LiDAR techniques for the generation of dense point clouds in many applications. However, dealing with large image sets is computationally demanding. It requires high performance hardware and often long processing times that makes the photogrammetric point cloud generation not suitable for mapping purposes at regional and national scale. These limitations are partially overcome by commercial solutions, thanks to the use of expensive and dedicated hardware. Nonetheless, a Free and Open-Source Software (FOSS) photogrammetric solution able to cope with these limitations is still missing.MethodsIn this paper, the bottlenecks of the basic components of photogrammetric workflows -tie-points extraction, bundle block adjustment (BBA) and dense image matching- are tackled implementing FOSS solutions. We present distributed computing algorithms for the tie-points extraction and for the dense image matching. Moreover, we present two algorithms for decreasing the memory needs of the BBA. The various algorithms are deployed on different hardware systems including a computer cluster.Results and conclusionsThe usage of the algorithms presented allows to process large image sets reducing the computational time. This is demonstrated using two different datasets.
Highlights
In the last decade Photogrammetry has shown to be a valid alternative to LiDAR techniques for the generation of dense point clouds in many applications
Photogrammetry has been living a second life pushed by the recent developments in Computer Vision, providing very dense and accurate point clouds compared to LiDAR techniques
Distributed computing In the previous section we proposed two algorithms to tackle the memory requirements of the bundle block adjustment (BBA) when dealing with large image sets: the solution is to reduce the amount of processed data to a lower, but sufficient, number of tie-points
Summary
In the last decade Photogrammetry has shown to be a valid alternative to LiDAR techniques for the generation of dense point clouds in many applications. Dealing with large image sets is computationally demanding It requires high performance hardware and often long processing times that makes the photogrammetric point cloud generation not suitable for mapping purposes at regional and national scale. These limitations are partially overcome by commercial solutions, thanks to the use of expensive and dedicated hardware. Photogrammetry is replacing range techniques in many applications (such as archaeology, geology, etc.) due to their reduced costs and the introduction of turnkey platforms such as unmanned aerial vehicles (UAVs) that makes possible the acquisition of large datasets. Most of the solutions concentrated on the generation of 3D models on relatively small areas, and solutions for point cloud generation at a regional or national level are still unluckily limited
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