The segmentation of point cloud play a key role in the processing of point cloud data, the regional growth is widely used in three-dimensional point cloud segmentation because it is easy to implement and use. However, due to the uncertainty of the point cloud characteristics and the unreasonable selection of seed point, the traditional regional growth method has the instability of local segmentation performance. To resolve this problem, an improved method of regional growth segmentation is presented, we set the minimum curvature point to the seed point by estimating the magnitude of the curvature of point cloud data. The reason for this is that the point with the minimum curvature is located in the flat area ,growth from the flattest area can reduce the total number of segments, then the growth criteria is determined according to the local characteristics of point cloud data. Experimental results show that this method can divide the point cloud data effectively, solve the problem of the instability of the traditional regional growth, and improve the accuracy and reliability of point cloud segmentation.