3DGS

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.

在正畸治疗中，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，三维高斯展铺（3DGS）技术的进展在三维重建和新型视图合成方面显示出强大的潜力。然而，传统的3DGS流程通常依赖于密集捕获的多视图输入和精确初始化的相机姿态，这限制了其实际应用的实用性。相比之下，正畸病例通常仅包含三张稀疏图像，即前视图和双侧颊视图，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，而相机姿态信息的缺失进一步复杂化了这一过程。为了克服这些局限性，我们提出了DentalSplat，这是一个从稀疏正畸图像中进行三维重建的有效框架。我们的方法利用先验知识引导的密集立体重建模型来初始化点云，然后采用尺度自适应的修剪策略来提高3DGS的训练效率和重建质量。在极端稀疏视角的情况下，我们进一步将光流纳入几何约束，结合梯度正则化，以提高渲染的真实性。我们在包含950个病例的大规模数据集和额外设计的195个病例的视频测试集上验证了我们的方法，以模拟现实世界的远程正畸成像条件。实验结果表明，我们的方法能够有效地处理稀疏输入场景，并在牙齿咬合可视化方面实现优质的新型视图合成，优于最新的技术。

论文及项目相关链接

PDF

Summary

本文介绍了在远程医疗背景下，针对正畸治疗中的牙齿咬合重建问题，利用3D高斯贴图技术提出了一种新的框架（DentalSplat）。该研究针对现有技术难以处理稀疏输入图像的问题，引入了一种新的重建策略，结合立体重建模型与自适应优化算法，并在极端稀疏视角条件下加入光学流场作为几何约束以提高渲染效果。实验结果证实该策略能出色处理稀疏输入情况并实现高质量的新型视图合成效果。这些技术在提高临床诊断和治疗精度上展示出巨大的潜力。

Key Takeaways

远程医疗背景下，正畸治疗中的牙齿咬合重建至关重要。
传统3DGS技术在处理稀疏输入图像时存在局限性。
提出的DentalSplat框架结合了立体重建模型与自适应优化算法，有效处理稀疏正畸图像。
在极端稀疏视角条件下引入光学流场作为几何约束，增强渲染效果。
实验结果证实该策略在新型视图合成质量上优于现有技术。

Cool Papers

点此查看论文截图

ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS

Authors:Weijie Wang, Donny Y. Chen, Zeyu Zhang, Duochao Shi, Akide Liu, Bohan Zhuang

Feed-forward 3D Gaussian Splatting (3DGS) models have recently emerged as a promising solution for novel view synthesis, enabling one-pass inference without the need for per-scene 3DGS optimization. However, their scalability is fundamentally constrained by the limited capacity of their models, leading to degraded performance or excessive memory consumption as the number of input views increases. In this work, we analyze feed-forward 3DGS frameworks through the lens of the Information Bottleneck principle and introduce ZPressor, a lightweight architecture-agnostic module that enables efficient compression of multi-view inputs into a compact latent state $Z$ that retains essential scene information while discarding redundancy. Concretely, ZPressor enables existing feed-forward 3DGS models to scale to over 100 input views at 480P resolution on an 80GB GPU, by partitioning the views into anchor and support sets and using cross attention to compress the information from the support views into anchor views, forming the compressed latent state $Z$. We show that integrating ZPressor into several state-of-the-art feed-forward 3DGS models consistently improves performance under moderate input views and enhances robustness under dense view settings on two large-scale benchmarks DL3DV-10K and RealEstate10K. The video results, code and trained models are available on our project page: https://lhmd.top/zpressor.

前馈三维高斯拼贴（3DGS）模型最近作为合成新视角的一种有前途的解决方案而出现，它能够实现一次推断，无需针对每个场景的3DGS进行优化。然而，其可扩展性从根本上受到模型容量的限制，随着输入视角数量的增加，性能会下降或内存消耗过大。在这项工作中，我们通过信息瓶颈原理分析前馈3DGS框架，并引入ZPressor，这是一个轻量级的架构无关模块，能够将多视角输入有效地压缩成紧凑的潜在状态Z，同时保留关键场景信息并丢弃冗余信息。具体来说，ZPressor通过将视角划分为锚点集和支持集，并使用交叉注意力将支持视角的信息压缩到锚点视角，形成压缩的潜在状态Z，从而使现有的前馈3DGS模型能够在80GB的GPU上以480P分辨率扩展到超过100个输入视角。我们在两个大规模基准测试DL3DV-10K和RealEstate10K上展示，将ZPressor集成到几种最新的前馈3DGS模型中，在适度的输入视角下一致地提高了性能，并在密集视角设置下增强了稳健性。视频结果、代码和训练模型可在我们的项目页面查看：https://lhmd.top/zpressor。

论文及项目相关链接

PDF NeurIPS 2025, Project Page: https://lhmd.top/zpressor, Code: https://github.com/ziplab/ZPressor

Summary

本文研究了基于信息瓶颈原理的Feed-forward 3D Gaussian Splatting（3DGS）模型，并引入了一种轻量级架构无关模块ZPressor。ZPressor能够将多视角输入压缩成紧凑的潜在状态Z，保留场景的关键信息并舍弃冗余信息，从而提高模型的扩展性。通过分区视角并使用交叉注意力机制压缩辅助视角信息到锚点视角，形成了压缩潜在状态Z。ZPressor与多个最先进的Feed-forward 3DGS模型集成后，在大型数据集DL3DV-10K和RealEstate10K上的表现得到了一致提升。

Key Takeaways