The stereo image acquisition capability of spaceborne platforms facilitates the generation of Digital Surface Models (DSMs), which provides great knowledge for assessing the aboveground objects. Thus, the satellite images are relevant for information extraction about the Earth surface. For the urban environment, building information extraction and reconstruction represents an imperative challenge that should be addressed since facilitates the development of further remote sensing applications. The study aims to improve the accuracy of 3D building models created based on stereo Pléiades satellite imagery, having as the main step the use of an accurate DTM in the dense image matching process of stereo Pléiades satellite imagery. In addition, the impact of the DTM on the dense image matching process for deriving DSM and DTM point clouds, together with the use of Pléiades-DSM point clouds for automatic creation of the LOD1 building 3D models in a GIS environment, is analyzed. For 3D building reconstruction, a prismatic model at LOD1 was chosen starting from existing buildings subfootprints. In this regard, for the point cloud generation, two scenarios were considered: (1) no DTM and ground control points (GCPs) with uncorrected ellipsoidal heights resulting in an RMS difference (Z) for the 64 GCPs and 78 check points (ChPs) of 69.8 cm and (2) with low resolution ALS-DTM and GCPs with corrected ellipsoidal height values resulting in an RMS difference (Z) of 60.9 cm. Considering the DTM and the DSM as the main dataset input for 3D building reconstruction, high importance should be given to the dense image matching process. By introducing previously available surface elevation information in the image matching process as a prediction parameter improves the robustness of the entire process, considerably reducing the systematic effects.