Accurate segmentation of 3D point cloud indoor plane elements is the basis of automatic reconstruction of indoor models. Aiming at the problem that the point cloud of boundary region is easily segmented incorrectly when the existing region growth algorithm is segmented indoor plane, a plane segmentation method with boundary feature constraint is proposed. This method first uses Euclidean clustering method to fuse RGB information to cluster the components that do not participate in plane segmentation, such as indoor tables and chairs, and then performs plane segmentation on the remaining point cloud. First, the region growth algorithm is used to segment the internal points in the plane, and then the growth process of seed points is monitored to accurately identify the boundary points in the neighborhood of seed points in the boundary region. The plane points are segmented by boundary points as growth constraint limits. Two sets of point cloud data in different scenes were used for experimental analysis. The test results show that the segmentation algorithm can accurately classify the point cloud in the boundary region, avoiding the problem of over -segmentation and under-segmentation of the point cloud. The accuracy and integrity of plane segmentation are improved by about 3% and 4% respectively compared with the region growth algorithm. The proposed algorithm can effectively improve the segmentation accuracy of indoor scenes.