From Point Clouds to Coherent Rooms: A Topology-Driven Approach for Indoor Space Subdivision

Modern indoor spaces increasingly contain curved walls, slanted surfaces, nested rooms and other non-Manhattan structures, making room-level subdivision from 3D point clouds challenging. Existing projection-based, primitive-based and semantic methods often rely on Manhattan assumptions, explicit structural labels or local geometric heuristics, which may lead to fragmented spatial units and unstable boundaries in complex scenes. Here, we propose a topology-driven voxel partitioning framework that reformulates indoor space subdivision as controlled connectivity disconnection within a topological closure. The central idea is to first construct a closed voxelated space domain and then selectively disconnect it at near functional openings and topological bottlenecks, rather than partitioning space only from local geometric cues. Within this framework, classical operations are reorganized under topological constraints as follows: adaptive region growing with aperture-sensitive spherical kernels generates initial spatial units, boundary-anisotropic watershed completion restores unlabeled boundary regions within interior domain, and label reassignment regularizes shared interfaces by minimizing discrete contact areas. Experiments on real-world and synthetic datasets show that the method produces stable room-level subdivisions across most Manhattan and non-Manhattan scenes. Cases with narrow bottlenecks or abrupt geometric narrowing still reduce detection-level precision and recall, indicating remaining limitations in highly constrained spatial configurations. Overall, the proposed framework offers a useful topological modeling perspective for indoor space subdivision in complex non-Manhattan environments.