The field of digital orthodontics and craniofacial identification is moving towards more robust and accurate methods for 3D reconstruction, structured dental understanding, and registration of intraoral scan models with cephalometric radiographs. Researchers are exploring innovative approaches to address the challenges of sparse-view photography, inconsistent illumination, and geometric distortion. Notably, geometry-aware pairing strategies and graph-guided pipelines are being developed to improve the stability of geometry initialization and reduce memory usage. Additionally, knowledge-guided frameworks are being proposed to standardize raw 3D meshes and constrain symbolic reasoning spaces. These advancements have the potential to enhance digital orthodontics and craniofacial identification, enabling more accurate diagnoses and treatments. Noteworthy papers include: Dental3R, which proposes a pose-free, graph-guided pipeline for robust, high-fidelity reconstruction from sparse intraoral photographs. ArchMap, which introduces a geometry-aware arch-flattening module and a Dental Knowledge Base to constrain symbolic reasoning spaces. DentalSCR, which proposes a pose-stable, contour-guided framework for accurate and interpretable silhouette-to-contour registration. Cranio-ID, which proposes a novel framework for graph-based craniofacial identification via automatic landmark annotation in 2D multi-view X-rays.