HASP-Net: Manifold-Guided Point-Cloud Segmentation of High-Aspect-Ratio Scences

Point cloud segmentation has advanced substantially in recent years, yet scenes containing high-aspect-ratio (HAR) structures and small attached components remain challenging. In these scenes, elongated structures often extend far beyond local neighborhoods while lying close to geometrically similar objects, making it difficult to simultaneously preserve long-range continuity and avoid feature interference from adjacent structures. Sparse sampling and scene cropping further weaken structural cues, while conventional point-wise metrics may underrepresent errors such as local breaks and missed attachments. To address this gap, we propose HASP-Net, a manifold-guided segmentation framework built on PTv3 for modeling long-range structural continuity and local semantic variation. The Long-range Track Module represents elongated structures as low-dimensional manifolds in 3D space, constructs geometry-consistent support chains, and propagates context bidirectionally along them to capture long-range dependencies while reducing interference from nearby structures. The Geometry-guided Interface Module focuses on regions with complex local manifold geometry and refines their features through geometry-aware point-set reasoning, reliability-gated residual updates, and semantic contrastive supervision. Together, the two modules jointly model long-range manifold structure and local geometric variation for improved segmentation of elongated structures and their attached components. However, existing benchmarks typically focus on specific domains and lack a unified setting for evaluating these challenges. We introduce HAR3D-Seg, a unified benchmark covering transmission, railway, and vascular scenes, with the Semantic Continuity Score (SCS) and Cross-class Adjacency Score (CAS) for structural evaluation. Averaged across the three scene types, HASP achieves $74.17\%$ mIoU, $80.92\%$ mAcc, $96.16\%$ OA, $2.95$ SCS, and $1.13$ CAS, demonstrating strong segmentation performance across diverse scenes.