The use of Airborne Laser Scanner (ALS) point clouds has dominated 3D buildings reconstruction research, thus giving photogrammetric point clouds less attention. Point cloud density, occlusion and vegetation cover are some of the concerns that promote the necessity to understand and question the completeness and correctness of UAV photogrammetric point clouds for 3D buildings reconstruction. This research explores the potentials of modelling 3D buildings from nadir and oblique UAV image data vis a vis airborne laser data. Optimal parameter settings for dense matching and reconstruction are analysed for both UAV image-based and lidar point clouds. This research employs an automatic data driven model approach to 3D building reconstruction. A proper segmentation into planar roof faces is crucial, followed by façade detection to capture the real extent of the buildings’ roof overhang. An analysis of the quality of point density and point noise, in relation to setting parameter indicates that with a minimum of 50 points/m², most of the planar surfaces are reconstructed comfortably. But for smaller features than dormers on the roof, a denser point cloud than 80 points/m² is needed. 3D buildings from UAVs point cloud can be improved by enhancing roof boundary by use of edge information from images. It can also be improved by merging the imagery building outlines, point clouds roof boundary and the walls outline to extract the real extent of the building.