As-Is Building Information Models aid in the management, maintenance and renovation of existing buildings. However, most existing buildings do not have an accurate geometric depiction of their As-Is conditions. The process of generating As-Is models of existing structures involves practitioners, who manually convert Point Cloud Data (PCD) into semantically meaningful 3D models. This process requires a significant amount of manual effort and time. Previous research has been able to model objects by segmenting the point clouds into planes and classifying each one separately into classes, such as walls, floors and ceilings; this is insufficient for modelling, as BIM objects are comprised of multiple planes that form volumetric objects. This thesis introduces a novel method that focuses on the geometric creation of As-Is BIMs with enriched information. It tackles the problem by detecting objects, modelling them and enriching the model with spaces and object adjacencies from PCD. The first step of the proposed method detects objects by exploiting the relationships the segments should satisfy to be grouped into one object. It further proposes a method for detecting slabs with variations in height by finding local maxima in the point density. The second step models the geometry of walls and finally enriches the model with closed spaces encoded in the Industry Foundation Classes (IFC) standard. The method uses the point cloud density of detected walls to determine their width by projecting the wall into two directions and finding the edges with the highest density. It identifies adjacent walls by finding gaps or intersections between walls and exploits walls adjacency for correcting their boundaries, creating an accurate 3D geometry of the model. Finally, the method detects closed spaces by using a shortest-path algorithm. The method was tested on three original PCD which represent office floors. The method detects objects of class walls, floors and ceilings in PCD with an accuracy of approximately 96%. The precision and recall for the room detection were found to be 100%.