We propose a novel hierarchy construction algorithm for 3D scenes with repeated elements, such as classrooms with multiple desk–chair pairs. Most existing algorithms focus on scenes such as bedrooms or living rooms, which rarely contain repeated patterns. Consequently, such methods may not recognize repeated patterns, which are vital for understanding the structure and context of scenes such as classrooms. Therefore, we propose a new global optimization algorithm for recognizing repeated patterns and building hierarchical structures based on repeated patterns. First, we find a repeated template by calculating the coverage ratios and frequencies of many substructures in a scene. Once the repeated template has been determined, a minimum cost maximum flow problem can be solved to find all instances (repetitions) of it in the scene and then group objects accordingly. Second, we group objects in the region outside the repeated elements according to their adjacency. Finally, based on these two sets of results, we build the hierarchy of the entire scene. We test this hierarchy construction algorithm on the Princeton and SceneNN databases and show that our algorithm can correctly find repeated patterns and construct a hierarchy that is more similar to the ground truth than the results of previous methods.