TSP-Net: From Structural Asymmetry to Topology-Preserved Symmetry for Occlusion-Robust Person Re-Identification

Occlusion introduces severe structural asymmetry into pedestrian representations by corrupting body topology, breaking cross-scale semantic continuity, and destabilizing identity geometry. Rather than treating occluded person re-identification (ReID) as a local visibility completion problem, this work reformulates it as a topology-preserved symmetry restoration problem: recovering symmetric identity structure from asymmetrically corrupted observations. Under this view, we present the Topology-Stable Person Re-identification Network (TSP-Net), a unified visual framework with three coordinated components: structural restoration, cross-scale symmetry alignment, and prototype-stabilized identity geometry. Specifically, Topology-Guided Occlusion and Visibility Modeling (TOVM) serves as the structural restoration component, and is realized by a closed loop of the Topology-Aware Occlusion Simulator (TOS) and the Topology-Aware Visibility Estimation (TVE) branch; Semantic-Anchored Cross-Scale Fusion (SACF) performs symmetry-consistent semantic recovery across hierarchical features; and the Prototype-Stabilized Supervision Loss (PSS Loss) regularizes identity embeddings toward topology-consistent manifold centers through momentum-updated prototypes. Experimental results on both occluded and holistic benchmarks show that TSP-Net is effective for learning occlusion-robust person representations. These findings suggest that restoring topology-preserved symmetry is a promising route for robust person re-identification under structural corruption.