<p>Automatic full-arch tooth segmentation and identification from intraoral scan (IOS) meshes is a fundamental task in digital orthodontics and restorative processes. However, it presents a structural difficulty: inter-tooth boundaries are thin and require precise local reasoning, while tooth identity depends on global arrangement and long-range geometric context along the dental arch. Existing mesh-based methods lack geometrically aligned cross-resolution correspondence. In this paper, we propose a Self-Parameterization Multi-Scale Mesh Segmentation Network (<b>SPMMSegNet</b>), which is an end-to-end mesh-native architecture designed to address this challenge through scale-structured propagation. Feature aggregation is restricted to intrinsic surface topology at fine resolutions to preserve boundary detail, while Euclidean aggregation is introduced at the coarsest scale to capture intra-tooth and arch-level context. The use of bijective surface correspondence enables geometrically consistent cross-scale feature transport. Specifically designed arch-aware positional encoding and an order-consistency regularization further incorporate anatomical structure into learning. By combining surface topology, global context, and anatomical priors within a unified mesh-based framework, SPMMSegNet provides an effective solution for full-arch tooth identification.</p>