A LiDAR point object primitive obtaining method still encounters challenges, such as large computation amount and ineffective segmentation for different building roof planes. A point object primitive obtaining method based on multiconstraint graph segmentation is proposed to address these challenges. A graph-based segmentation strategy is adopted for this method. First, constraint conditions of adjacent points are used to construct a network graph structure to reduce the complexity of the graph and improve the efficiency of the algorithm. Subsequently, the angle of the normal vectors of adjacent nodes is constrained using a threshold value to divide the point cloud located in the same plane into the same object primitive. Finally, the maximum side length constraint is performed to separate the building point cloud from its adjacent vegetation points. Three sets of public test data provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) and two datasets located in Wuhan University were selected for testing to verify the validity of the proposed method. Experimental results show that the proposed method can effectively divide different roof planes of buildings. DBSCAN and spectral clustering methods were used for comparison, and precision, recall, and F1 score were adopted as evaluation indexes. Compared with the other two methods, the proposed method achieves the best overall segmentation results in case of the five datasets with different building environments, with better recall and F1 score.