Three-dimensional point cloud registration is important in reverse engineering. In this paper, we propose a registration method for large-scale 3D point clouds, which is based on neighborhood constraints of geometrical features. The method consists of initial and exact registration steps.In the process of initial registration, we define a new functon that measures feature similarity by calculating the distance function, and in the process of exact registration, we introduce the angle information that improve the accuracy of iterative closest point algorithm. Compared with the traditional feature-based and iterative closest point algorithms, our method significantly reduced the registration time by 11.9 % and has only 1 % of the registration error of the traditional feature-based algorithm. The proposed algorithm can be used to create efficient 3D models for virtual plant reconstruction and computer-aided design, and the registration results can provide a reference for virtual plant reconstruction and growth.