发布日期: 2025-11-07

更新日期: 2025-11-07

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2025-11-07 更新

DentalSplat: Dental Occlusion Novel View Synthesis from Sparse Intra-Oral Photographs

Authors:Yiyi Miao, Taoyu Wu, Tong Chen, Sihao Li, Ji Jiang, Youpeng Yang, Angelos Stefanidis, Limin Yu, Jionglong Su

In orthodontic treatment, particularly within telemedicine contexts, observing patients’ dental occlusion from multiple viewpoints facilitates timely clinical decision-making. Recent advances in 3D Gaussian Splatting (3DGS) have shown strong potential in 3D reconstruction and novel view synthesis. However, conventional 3DGS pipelines typically rely on densely captured multi-view inputs and precisely initialized camera poses, limiting their practicality. Orthodontic cases, in contrast, often comprise only three sparse images, specifically, the anterior view and bilateral buccal views, rendering the reconstruction task especially challenging. The extreme sparsity of input views severely degrades reconstruction quality, while the absence of camera pose information further complicates the process. To overcome these limitations, we propose DentalSplat, an effective framework for 3D reconstruction from sparse orthodontic imagery. Our method leverages a prior-guided dense stereo reconstruction model to initialize the point cloud, followed by a scale-adaptive pruning strategy to improve the training efficiency and reconstruction quality of 3DGS. In scenarios with extremely sparse viewpoints, we further incorporate optical flow as a geometric constraint, coupled with gradient regularization, to enhance rendering fidelity. We validate our approach on a large-scale dataset comprising 950 clinical cases and an additional video-based test set of 195 cases designed to simulate real-world remote orthodontic imaging conditions. Experimental results demonstrate that our method effectively handles sparse input scenarios and achieves superior novel view synthesis quality for dental occlusion visualization, outperforming state-of-the-art techniques.

在牙齿矫正治疗中，特别是在远程医疗的背景下，从多个角度观察患者的咬合情况有助于及时做出临床决策。最近三维高斯摊铺技术（3DGS）的进步在三维重建和新颖视图合成方面显示出强大的潜力。然而，传统的三维重建管道通常依赖于密集捕捉的多视图输入和精确初始化的相机姿态，这限制了它们的实用性。相比之下，牙齿矫正的情况通常只包含三张稀疏图像，即正面和两侧面部视图，这使得重建任务尤其具有挑战性。输入视图的极端稀疏性严重降低了重建质量，而缺少相机姿态信息则进一步使过程复杂化。为了克服这些局限性，我们提出了DentalSplat框架，这是一个有效的从稀疏正畸图像中进行三维重建的框架。我们的方法利用先验引导的密集立体重建模型来初始化点云，随后采用尺度自适应修剪策略来提高三维重建的训练效率和质量。在极端稀疏视角的场景下，我们进一步将光流作为几何约束纳入其中，结合梯度正则化，以增强渲染的真实性。我们在包含950个临床病例的大规模数据集和模拟真实世界远程正畸成像条件的额外基于视频的测试集上验证了我们的方法。实验结果表明，我们的方法能够有效地处理稀疏输入场景，并在牙齿咬合的可视化方面实现了优质的新视角合成效果，优于最新的技术。

论文及项目相关链接

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Summary

本文探讨了在远程医疗环境下利用新技术对牙科病人的牙咬合情况进行三维重建的挑战与解决方案。针对传统三维重建技术依赖于多视角图像和精确相机姿态的问题，提出了一种新的方法——DentalSplat框架。该方法结合立体重建模型和规模自适应修剪策略，即使在极端稀疏视角情况下也能实现高质量的三维重建。此外，还引入了光学流作为几何约束，以提高渲染的真实感。实验结果表明，该方法在稀疏输入场景下表现优异，实现了高质量的牙咬合情况可视化。

Key Takeaways