The laser scanner can precisely and efficiently conduct the three-dimensional reconstruction of the building's interior, and data registration of each station can offer complete 3D information. A point cloud registration approach based on curvature threshold is proposed to tackle the difficulty of registration with a large amount of point cloud data and a low coincidence rate. The point cloud's normal vector is employed to estimate the point's curvature. To obtain the input point cloud's characteristic point set, a suitable curvature's threshold is set to simplify the point cloud and take it as the characteristic point set. The point cloud registration algorithm that is based on probability distribution is adopted for coarse registration, to quickly and effectively conduct the point cloud's preliminary registration. For precise registration, the iterative closest point algorithm of KD-Tree acceleration is employed. Through comparison time and precision analysis with classic SAC-IC and other algorithms, the experimental results demonstrate that the registration accuracy is enhanced by more than 35% in scenes with a large amount of point cloud data and low coincidence rate, and registration time is enhanced by more than 30%, and reconstruction efficiency is enhanced by more than 30%.