Accurate detection of tooth landmarks is crucial for computer-aided orthodontic treatment. Previous methods often employ segmentation to isolate individual teeth, but rely heavily on segmentation accuracy and require annotated data. In this paper, we introduce a two-stage framework for tooth localization and landmark detection, eliminating the need for segmentation based on mesh deep learning. First, we define the fuzzy tooth regions based on landmark positions. Binary masks are generated for the tooth regions located from the original jaw mesh. By combining local features of individual teeth with the global features of the jaw model, our method predicts multiple heatmaps and the corresponding probabilities of potential landmarks for each tooth. Finally, we design a bipartite matching loss for both tooth localization and landmark detection to align the prediction set with the ground truth, thereby facilitating end-to-end inference throughout the entire process. Experimental results on the Teeth3DS+ dataset demonstrate that our method effectively detects a variable number of landmarks. Furthermore, it significantly outperforms existing baseline methods, exhibiting robust generalization and superior performance. (The code will be released at https://github.com/sikingbo/ToothLDNet .)