Three dimensional (3D) imaging technologies have been increasingly used in academia and in the industrial sector, especially in the form of point clouds, a mathematical representation of the geometry and surface of an object or area. However, obtaining this data can still be expensive and time consuming, reducing the efficiency of many procedures dependent on a large set of point clouds, such as the generation of datasets for machine learning training, forest canopy calculation and subsea survey. A trending solution is the development of computer simulators for imaging systems, performing the virtual scanning of a scenario made from 3D object files. At the end of this process, synthetic point clouds are obtained. This work presents the development of a LiDAR system simulator (light detection and ranging) based on parallel ray tracing algorithms (GPU raytracing), with its virtual sensor modeled by metrological parameters. A way of calibrating the sensor is displayed, by comparing it with the measurements of a real LiDAR sensor, in addition to surveying error models to increase the realism of the virtual scan. A flexible scenario creator was also implemented to facilitate interaction with the user. The combination of these tools in the simulator resulted in a robust generation of synthetic point clouds in different scenarios, enabling the creation of datasets for use in concept tests, combining real and virtual data, among other applications.