Many natural and engineered systems can be described as latent structural networks embedded in spatial point clouds. Examples include the cosmic web of galaxies, vascular systems, industrial pipelines, road networks and LiDAR-derived urban structures. Despite their diversity, these systems often share a common geometric signature: elongated filamentary structures connected through branching nodes. We introduce the Structural Network Engine, a domain-agnostic computational framework designed to extract structural backbones, topology graphs, trunk paths and structural metrics directly from noisy 3D point clouds without requiring domain-specific assumptions. The method combines local neighborhood graph construction, anisotropy-based backbone detection, weighted graph inference, component reconnection and topology reduction. We evaluate the approach across multiple synthetic and domain-inspired datasets including cosmic-like, vascular, urban, industrial and random spatial distributions. Results show robust separation between structured and random data, strong multi-domain behavior and approximate scale invariance under geometric rescaling.
Nathan Bili Toponi (Wed,) studied this question.