Foundation models in computer vision, such as the Segment Anything Model (SAM), have demonstrated remarkable zero-shot performance in image segmentation. Leveraging these models for automated building segmentation can contribute to the efficiency of Scan-to-BIM workflows. Automatic 3D modelling has become widely relied on point cloud data; however, the nature of this data hinders the direct application of the foundation models. This study explores the potential use of SAM for automatic point cloud segmentation, proposing a SAM-based approach for segmenting building components, such as rooms, doors, and windows. The proposed method employs SAM to generate masks for an image that represents projected point clouds. Point clouds are then retrieved for each mask, which are further classified to identify building components. Room segmentation starts with the extraction of a section that defines the room boundary, followed by horizontal projection of the section. In contrast, door and window segmentation starts by projecting planes containing wall points onto their normal vectors. The experiments have been performed using three real case studies. The findings demonstrate the method's effectiveness without requiring any pretraining process, highlighting that the application of the foundation models in point cloud segmentation is a promising direction.