Dental3R：基于几何感知配对的稀疏视图照片口腔内3D重建
Dental3R: Geometry-Aware Pairing for Intraoral 3D Reconstruction from Sparse-View Photographs
AI总结：
针对远程正畸中稀疏口腔照片3D重建的挑战，提出Dental3R，通过几何感知配对策略（GAPS）提升初始化稳定性，结合小波正则化3DGS模型，在大规模临床数据集上实现更优的高保真重建与新视图合成。
摘要：
口腔内3D重建是数字正畸学的基础，但口内扫描等传统方法在依赖稀疏智能手机图像的远程正畸中难以应用。3D高斯溅射（3DGS）虽在新视图合成中表现出潜力，但其应用于无姿态前牙和双侧颊侧照片的标准临床三联组仍具挑战。口腔内场景中常见的大视角基线、不一致照明和镜面表面会破坏姿态与几何的同时估计稳定性。此外，稀疏视图光度监督常引发频率偏差，导致过度平滑的重建结果，丢失关键诊断细节。为此，我们提出Dental3R，一种无姿态、图引导的管道，用于从稀疏口腔内照片进行鲁棒、高保真的重建。该方法首先构建几何感知配对策略（GAPS），智能选择高价值图像对的紧凑子图。GAPS聚焦于对应匹配，从而提升几何初始化稳定性并降低内存使用。基于恢复的姿态和点云，我们用小波正则化目标训练3DGS模型。通过离散小波变换强制带限保真度，该方法在抑制高频伪影的同时，保留了精细的牙釉质边界和邻面边缘。我们在含950个临床病例的大规模数据集和含195个病例的额外视频测试集上验证了该方法。实验结果表明，Dental3R有效处理稀疏、无姿态输入，在牙科咬合可视化的新视图合成质量上优于现有方法。
英文摘要：
Intraoral 3D reconstruction is fundamental to digital orthodontics, yet conventional methods like intraoral scanning are inaccessible for 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 large view baselines, inconsistent illumination, and specular surfaces common in intraoral settings can destabilize simultaneous pose and geometry estimation. Furthermore, sparse-view photometric supervision often induces a frequency bias, leading to over-smoothed reconstructions that lose critical diagnostic details. To address these limitations, we propose \textbf{Dental3R}, a pose-free, graph-guided pipeline for robust, high-fidelity reconstruction from sparse intraoral photographs. Our method first constructs a Geometry-Aware Pairing Strategy (GAPS) to intelligently select a compact subgraph of high-value image pairs. The GAPS focuses on correspondence matching, thereby improving the stability of the geometry initialization and reducing memory usage. Building on the recovered poses and point cloud, we train the 3DGS model with a wavelet-regularized objective. By enforcing band-limited fidelity using a discrete wavelet transform, our approach preserves fine enamel boundaries and interproximal edges while suppressing high-frequency artifacts. We validate our approach on a large-scale dataset of 950 clinical cases and an additional video-based test set of 195 cases. Experimental results demonstrate that Dental3R effectively handles sparse, unposed inputs and achieves superior novel view synthesis quality for dental occlusion visualization, outperforming state-of-the-art methods.
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