ExtraGS：通过扩散引导的3D高斯点渲染增强内窥镜视图外推
ExtraGS: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting
- The School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, and the Shanghai Key Laboratory of Navigation and Location-Based Services（上海交通大学自动化与智能感知学院以及上海市导航与位置服务重点实验室）
- Global College, Shanghai Jiao Tong University（上海交通大学密西根学院）
- Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属第九人民医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对传统内窥镜视野局限及神经渲染外推有伪影问题，提出ExtraGS框架，通过不确定性引导虚拟相机采样、扩散模型细化视图及置信加权微调策略，增强内窥镜视图外推，在新视图合成中达先进性能。
AI中文摘要：
机器人辅助微创手术严重依赖可靠的内窥镜感知以实现导航和安全。然而，传统内窥镜视野有限。近期神经渲染方法虽能实现新视图合成，但外推时易产生伪影。本文提出ExtraGS框架，通过扩散引导的3D高斯点渲染增强内窥镜视图外推。从初始重建开始，引入不确定性引导的虚拟相机采样策略探索盲点并最大化信息增益，用扩散模型细化渲染视图以恢复合理结构，采用置信加权微调策略防止生成内容影响可靠区域。实验表明ExtraGS显著减少外推伪影并在新视图合成中达到先进性能。
英文摘要：
Robot-assisted minimally invasive surgery (MIS) critically depends on reliable endoscopic perception for navigation and safety. However, conventional endoscopes provide only a limited field of view, leaving large portions of the surrounding anatomy unobserved. Recent neural rendering approaches, such as Neural Radiance Fields and 3D Gaussian Splatting, enable novel view synthesis from endoscopic videos, but their reliance on sparse observations often leads to severe artifacts when extrapolating beyond the training trajectory. In this work, we propose ExtraGS, a framework for enhancing endoscopic view extrapolation through diffusion-guided 3D Gaussian Splatting. Starting from an initial reconstruction, we introduce an uncertainty-guided virtual camera sampling strategy to actively explore blind spots and maximize information gain. The rendered views from these sampled locations are refined using a diffusion model to recover plausible anatomical structures, producing pseudo-observations that guide further optimization. To prevent the generated content from degrading reliable regions, we adopt a confidence-weighted fine-tuning strategy when incorporating these pseudo-observations. Extensive experiments on multiple public endoscopic datasets demonstrate that ExtraGS significantly reduces extrapolation artifacts and achieves state-of-the-art performance in endoscopic novel view synthesis.
