HRsR: Hierarchical Rotation System Reconstruction
This work improves scalability of topology-controlled mesh reconstruction for practitioners needing efficient processing of large point clouds.
HRsR accelerates Rotation System Reconstruction (RsR) for surface reconstruction from point clouds by using a hierarchical pipeline, achieving up to 6× speedup and over 8× memory reduction while maintaining comparable quality.
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.