Craniomaxillofacial deformities can substantially disrupt the quality of life, and treatments for these conditions present considerable complexity and risk. Intelligent surgical planning systems hold the potential to address these challenges effectively, and one key aspect lies in the accurate detection of anatomical landmarks within the craniomaxillofacial region. However, in clinical applications, landmark detection based on 2D images does not adequately represent the structural details of the craniomaxillofacial region. Landmark detection utilizing 3D models is largely confined to CBCT datasets, which exhibits a deficiency in adaptability to more general modeling platforms. We propose an attention-enhanced method based on common 3D models for automatically detecting craniomaxillofacial anatomical landmarks. The approach relies on the dimensional reduction of a 3D craniomaxillofacial model achieved through multi-angle projections under multi-light conditions. To enhance the robustness of our method, an attention module is introduced to integrate detection results from projections under diverse lighting conditions. Our method has undergone evaluation using data from professional surgeons at the Chinese Academy of Medical Sciences and Peking Union Medical College. The average error in landmark localization on the 3D model is successfully constrained to approximately 2.51 mm. From experimental evaluations and the user study, both the qualitative and quantitative results consistently indicate that our techniques can accurately detect 3D craniomaxillofacial anatomical landmarks.