Dental3R: Geometry-Aware Pairing for Intraoral 3D Reconstruction from Sparse-View Photographs

Digital orthodontics increasingly depends on accurate 3D dental models, yet conventional intraoral scanning remains inaccessible in remote tele-orthodontics, which typically relies on sparse smartphone imagery. While 3D Gaussian Splatting (3DGS) shows promise for novel view synthesis, its application to the standard clinical triad of unposed anterior and bilateral buccal photographs is challenging. The limitations of sparse-view photometric supervision, combined with large baselines, inconsistent illumination, and specular surfaces, can destabilize simultaneous pose and geometry estimation, often inducing frequency bias and over-smoothed reconstructions that lose critical diagnostic details. To address these issues, we propose Dental3R, a pose-free, graph-guided pipeline for robust, highfidelity reconstruction from sparse intraoral photographs. Our method first constructs a Geometry-Aware Pairing Strategy (GAPS) to select a compact subgraph of high-value image pairs, improving correspondence matching, stabilizing geometry initialization, and reducing memory usage. Leveraging on the recovered poses and point cloud, we train the 3DGS model with a wavelet-regularized objective. By enforcing band-limited fidelity via a discrete wavelet transform, our approach preserves fine enamel boundaries and interproximal edges while suppressing high-frequency artifacts. We validate our approach on a largescale dataset of 950 clinical cases and an additional video-based test set of 195 cases, demonstrating that Dental3R effectively handles sparse, unposed inputs and achieves superior novel-view synthesis quality over state-of-the-art methods.