###### Abstract  
There are currently several new technologies being used to generate digital elevation models that do not use photogrammetric techniques. For example, LiDAR (Laser Imaging Detection and Ranging) and RADAR (RAdio Detection And Ranging) can generate 3D points and reflectivity information of the surface without using a photogrammetric approach. In the case of LiDAR, the intensity level indicates the amount of energy that the object reflects after a laser pulse is transmitted. This energy mainly depends on the material and the wavelength used by LiDAR. This intensity level can be used to generate a synthetic image colored by this attribute (intensity level), which can be viewed as a RGB (red, green and blue) picture. This work presents the outline of an innovative method, designed by the authors, to generate synthetic pictures from point clouds to use in classical photogrammetric software (digital restitution or stereoscopic vision). This is conducted using available additional information (for example, the intensity level of LiDAR). This allows mapping operators to view the LiDAR as if it were stereo-imagery, so they can manually digitize points, 3D lines, break lines, polygons and so on. LiDARgrammetry; synthetic models; LiDAR; photogrammetry; radargrammetry +
Footnote †: journal: Applied  
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characteristic provided by the sensor used (LiDAR intensity level, terrain reflectivity, etc.). This is achieved assigning a \"false color\" to the images obtained in the process in order to ensure correct 3D visualization [2]. LiDARgrammetry is a term coined by GeoCue for using the intensity information as the source data for generating stereo-models of the project area directly from the LiDAR data itself rather than from ancillary imagery. By creating appropriate stereo-pairs from the LiDAR intensity images, it is possible to generate stereo-models that can be directly exploited using existing photogrammetric workstations and the traditional photogrammetric workflow in areas, such as feature extraction or break line compilation [3]. LiDARgrammetry concerns the production of inferred stereo-pairs from LiDAR intensity images, which aims to stereo-digitize the spatial data in digital photogrammetric stations. The production of inferred stereo-pairs is based on the principle of stereo ortho-mates and the extraction of a derivative intensity image, in which an artificial \(x\)-parallax is introduced. Furthermore, other techniques have also developed in order to fully utilize the 3D nature of LiDAR data. LiDARgrammetry aims to quantify the derivative spatial data quality and the impact of the reduced photo-interpretative ability of LiDAR data with comparisons to typical photogrammetric stereo-models [4]. However, this relatively new approach has not been yet assessed properly. The LiDARgrammetry technique is based on the inverse algorithm of the photogrammetric technique, which is namely the 3D dataset (point cloud) that is used to generate 2D information (synthetic images). Therefore, the resolution and definition of the images depend on the density (points per square meters) and radiometric characteristics (intensity level) of the LiDAR dataset as well as the resampling algorithm used. The images are generated as if they had been registered at a given position using the \"base-height\" ratio related to the dataset's parameters, which are used as a base reference. The objective of the work presented here is to design and develop a suitable methodology for generating and visualizing 3D images from point clouds. Within this context, the \"radargrammetry\" technique [5] can be considered, which is similar to the photogrammetric one as it allows us to display point clouds using photogrammetric stations via the generation of synthetic images.