Purpose <p>Accurate tooth segmentation and labeling play a crucial role in digital dental diagnostic systems. Despite advancements in 3D intra-oral scanning techniques, challenges remain in handling ambiguous boundaries at the gingival junction and complex dental conditions. The aim of this study is to introduce a novel 3D tooth segmentation network that utilizes multi-scale graph convolutional neural networks and cross-domain integration to address these limitations.</p> Methods <p>Our network proposes a deep learning-based dual-branch architecture to independently extract features of tooth coordinates and normal vectors. The coordinate branch captures fine-grained features across tooth structures through multi-scale graph convolution, thereby achieving accurate tooth boundary localization. The cross-domain fusion module (CFM) adaptively integrates features from the two branches through spatial attention and channel attention maps to enhance the representation of tooth boundaries and complex morphology.</p> Results <p>Evaluated on a public 3D IOS dataset, our method outperforms the state-of-the-art, achieving improvements of 2.38%, 2.66%, and 2.44% in OA, mIoU, and mAcc, respectively.</p> Conclusion <p>These results validate the effectiveness of our approach in enhancing the accuracy and robustness of 3D intra-oral tooth segmentation.</p>