Segmentation of dentomaxillofacial structures in Cone-Beam Computed Tomography (CBCT) remains challenging, particularly for fine details such as root apices and nerve canals, which are crucial for evaluating root resorption in digital dentistry or to make surgical planning more precise. We present an approach that unifies instance detection and multi-class dentomaxillofacial structure segmentation in CBCT scans, in the scope of the ToothFairy3 Challenge. We adapt a Deep Watershed method, modeling each anatomical structure as a continuous 3D energy basin encoding voxel distances to class boundaries. This instance-aware representation ensures accurate segmentation of narrow, complex dentomaxillofacial structures. We train and evaluate our solution on the ToothFairy3 dataset, comprising 532 CBCT scans with voxel-wise annotations. Our method achieved a mean Dice coefficient of 0.742 and HD95 of 111.13 on the test set. We provide implementation at https://github.com/tomek1911/TF3 .