In dental practice, accurate segmentation of oral and maxillofacial structures from cone-beam computed tomography (CBCT) images is essential for diagnostic and treatment planning purposes. However, manual segmentation is time-consuming and labor-intensive. Although numerous deep learning-based methods have been proposed to automate this process, most rely on a single model architecture, which struggles to handle the complex and diverse nature of oral anatomical structures. To address this limitation, we propose a hybrid framework integrating nnUNet and VISTA models for automated and interactive segmentation of oral and maxillofacial structures. Our approach employs a class-wise ensemble strategy to improve inference efficiency and accuracy, and incorporates post-processing techniques such as threshold-based small object removal and disconnected region filtering to enhance robustness. The proposed method achieved third place in Task 1 and second place in Task 2 of the ToothFairy3 Challenge. Code and model weights are available at https://github.com/ff741333/toothfairy3_blcakmyth .