Automatic teeth segmentation and labeling on dental models are basic tasks in computer-aided dentistry. Many existing works can achieve promising results in teeth segmentation, but they heavily rely on aligned input dental models, which leads to additional manual intervention. Moreover, tooth labeling is an essential task in digital dentistry for treatment planning ( e.g. , orthodontic), and is usually ignored in these methods. In this article, we propose an AlignNet for aligning dental models of arbitrary sizes and orientations automatically. Meanwhile, a multi-task hybrid learning network is designed that effectively plays the advantages of semantic segmentation and instance segmentation, and synergistically improves the performance of teeth point clouds segmentation and labeling. Particularly, for the teeth-gingival boundaries with large segmentation errors, we utilize the filtered curvature information as a constrained feature to detect the weak boundary more accurately. At last, we propose a DiffLoss and postprocessing step based on the dental arch to address the teeth classification problem. Through extensive evaluations of oral scanning models, our method is robust to handle dental model point clouds with arbitrary size and orientation, and outperforms state-of-the-art teeth segmentation and labeling methods, demonstrating its full automation and robustness in clinical practice.