Surgical 3D reconstruction is a critical area of research in robotic surgery, with recent works adopting variants of dynamic radiance fields to achieve success in 3D reconstruction of deformable tissues from single-viewpoint videos. However, these methods often suffer from time-consuming optimization or inferior quality, limiting their adoption in downstream tasks. Inspired by 3D Gaussian Splatting, a recent trending 3D representation, we present EndoGS, applying Gaussian Splatting for deformable endoscopic tissue reconstruction. Specifically, our approach incorporates deformation fields to handle dynamic scenes, depth-guided supervision with spatial-temporal weight masks to optimize 3D targets with tool occlusion from a single viewpoint, and surface-aligned regularization terms to capture the much better geometry. As a result, EndoGS reconstructs and renders high-quality deformable endoscopic tissues from a single-viewpoint video, estimated depth maps, and labeled tool masks. Experiments on DaVinci robotic surgery videos demonstrate that EndoGS achieves superior rendering quality. Code is available at https://github.com/HKU-MedAI/EndoGS.

翻译：手术三维重建是机器人手术领域的关键研究方向，近期工作采用动态辐射场变体方法，成功实现了从单视角视频中重建可变形组织的三维结构。然而，这些方法常面临优化耗时长或重建质量低的问题，限制了其在下游任务中的应用。受当前主流三维表示方法3D高斯泼溅的启发，我们提出EndoGS，将高斯泼溅应用于可变形内窥镜组织重建。具体而言，本方法融合了变形场以处理动态场景、采用含时空权重掩码的深度引导监督机制实现单视角下工具遮挡场景的三维目标优化、并通过表面对齐正则化项精确捕捉几何结构。最终，EndoGS能够基于单视角视频、估计深度图及标注工具掩码，重建并渲染高质量的可变形内窥镜组织。在达芬奇机器人手术视频上的实验表明，EndoGS实现了卓越的渲染质量。代码开源地址：https://github.com/HKU-MedAI/EndoGS。