Over the last decade, LIDAR techniques have replaced traditional photogrammetric techniques in many applications because of their speed in point cloud generation. However, these laser scanning techniques have non-negligible limits and, for this reason, many researchers have decided to focus on improving the performances of matching technique in order to generate dense point clouds from images.The first tests carried out at the Politecnico di Torino on the first fully-automated multi-image matching commercial software, the ZScan Menci Software, are described in this paper. This instrument was first devised to allow inexperienced users to generate very dense point clouds from image triplets; a customized calibrated bar (0,90 m length) is used for image acquisition. Recently a new version of ZScan has been created in order to elaborate triplets of oriented aerial images and generate DSM: the first results obtained in this way are presented in this paper. Several tests have been performed on the ZScan performances analysing different geometrical configurations (base-to-height ratio) and textures. The evaluation of the geometric precision obtained by this software in point cloud generation may help to understand which performances can be achieved with a fully automated multi-image matching. The evaluation concerns what the most critical aspects of these techniques are and what improvements will be possible in the future. Furthermore a possible new research project is described which has the aim of transferring useful information about breakline location from images to point clouds in order to derive automatically the segmentation algorithms"
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