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 to optimize 3D targets with a single viewpoint, and a spatial-temporal weight mask to mitigate tool occlusion. 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.
外科3D重建是机器人外科手术研究中的一个关键领域。近期的研究采用动态辐射场的变体，成功实现了从单视点视频中对可变形组织进行3D重建。然而，这些方法通常存在耗时优化或质量较低的问题，限制了它们在下游任务中的应用。受到近期流行的3D表示方法3D高斯溅射的启发，我们提出了EndoGS，将高斯溅射应用于可变形内窥镜组织重建。具体来说，我们的方法结合了变形场以处理动态场景，深度引导的监督来优化单视点的3D目标，以及空间-时间权重掩码来减轻工具遮挡。结果表明，EndoGS能够从单视点视频、估计的深度图和标记的工具掩码中重建和渲染高质量的可变形内窥镜组织。在DaVinci机器人外科手术视频上的实验表明，EndoGS实现了卓越的渲染质量。