The availability of active 3D sensing devices such as LiDAR has significantly increased the collection of 3D urban scenes with rich details. The sheer amount of data brings a lot of opportunities but also poses tremendous challenges for both academic research and industrial applications on point cloud classification and building reconstruction. In this paper, we present an online algorithm to automatically detect and segment buildings from large scale unorganized 3D point clouds of urban scenes acquired by ground-Based LiDAR devices. The core idea is that buildings can be observed in a street view separated by empty spaces such as alleys. By progressively projecting 3D points onto views along the scanning path, buildings can be detected as large regions with dense points. Experiments on several large scale datasets show that our approach can efficiently produce satisfactory results.