In this project we address the problem of compression of 3D models and reduction of the computational effort and the amount of data required for their processing. 3D models consist of point clouds created by 3D scans or the processing of several images of a particular object photographed from a variety of angles. These clouds contain a large amount of points that makes it difficult to store and process them efficiently. These models are used for many purposes such as mapping, navigation and a variety of applications of visualization and computer vision. Therefore, it is important to be able to process them efficiently.
The method we chose to perform the compression is based on representing a large number of points using geometric shapes. In this way a lot of points can be converted to a limited number of parameters that give a mathematical representation to the shape that represents them. We examine the results of the our method against existing methods while comparing the visual visibility of the result and the level of compression that can be achieved.