Surface reconstruction from point clouds remains challenging when both geometric fidelity and topology control are required. Rotation System Reconstruction (RsR) reconstructs triangle meshes from point clouds while explicitly controlling topology through the Euler characteristic, but its sequential edge insertion limits scalability. We present Hierarchical Rotation System Reconstruction (HRsR), which accelerates RsR through a hierarchical pipeline of edge collapses and vertex splits. HRsR first simplifies the input using a $k$-nearest neighbor graph, performs reconstruction on the reduced structure, and then restores geometric detail while preserving topology. To maintain geometric consistency, we incorporate intersection handling and quality-driven vertex split selection. Experiments demonstrate up to a $6\times$ speedup and more than $8\times$ reduction in memory usage over RsR, while achieving comparable reconstruction results.

翻译：从点云进行表面重建在需要同时满足几何保真度和拓扑控制时仍具挑战性。旋转系统重建通过欧拉示性数显式控制拓扑结构，从点云重建三角形网格，但其顺序边插入方式限制了可扩展性。我们提出层次化旋转系统重建，通过边坍缩与顶点分裂的层次化流水线加速RsR。HRsR首先利用$k$近邻图对输入进行简化，在降阶结构上执行重建，随后在保持拓扑的同时恢复几何细节。为维持几何一致性，我们引入相交处理与质量驱动的顶点分裂选择策略。实验表明，相较于RsR，HRsR实现了最高$6\times$的加速比与超过$8\times$的内存使用降低，同时获得可比较的重建结果。