Segmenting roof planes from airborne LiDAR point cloud data is essential for three-dimensional (3D) building reconstruction. However, the presence of discrete point clouds and complex roof structures present significant challenges to effective roof plane segmentation. To address this, a roof plane segmentation method that leverages multi-scale voxels and graph cuts is proposed. First, an octree voxelizes the original point cloud, generating multi-scale voxels based on geometric features to precisely characterize the data. Next, a bottom-up hierarchical clustering algorithm progressively merges these multi-scale voxels to obtain initial segmentation results. Finally, graph cuts refine the initial segmentation results and address boundary aliasing. The proposed method demonstrates superior performance, achieving roof plane segmentation accuracy of over 92.6% compared to four other methods. The proposed method accurately enables the generation of accurate roof planes for 3D building reconstruction.