SurgicalGS: Dynamic 3D Gaussian Splatting for Accurate Robotic-Assisted Surgical Scene Reconstruction
Accurate 3D reconstruction of dynamic surgical scenes from endoscopic video is essential for robotic-assisted surgery. While recent 3D Gaussian Splatting methods have shown promise in achieving high-quality reconstructions with fast rendering speeds, their use of inverse depth loss functions compresses depth variations. This can lead to a loss of fine geometric details, limiting their ability to capture precise 3D geometry and effectiveness in intraoperative application. To address these challenges, we present SurgicalGS, a dynamic 3D Gaussian Splatting framework specifically designed for surgical scene reconstruction with improved geometric accuracy. Our approach first initialises a Gaussian point cloud using depth priors, employing binary motion masks to identify pixels with significant depth variations and fusing point clouds from depth maps across frames for initialisation. We use the Flexible Deformation Model to represent dynamic scene and introduce a normalised depth regularisation loss along with an unsupervised depth smoothness constraint to ensure more accurate geometric reconstruction. Extensive experiments on two real surgical datasets demonstrate that SurgicalGS achieves state-of-the-art reconstruction quality, especially in terms of accurate geometry, advancing the usability of 3D Gaussian Splatting in robotic-assisted surgery.
从内窥镜视频中精确重建动态手术场景对于机器人辅助手术至关重要。尽管近期的3D高斯散射方法在实现高质量重建和快速渲染方面展现了潜力，但其使用的反深度损失函数压缩了深度变化，导致细微几何细节的丢失，限制了捕捉精确3D几何形状的能力，进而影响其在术中应用的效果。为了解决这些问题，我们提出了SurgicalGS，这是一个专为手术场景重建设计的动态3D高斯散射框架，能够提升几何精度。我们的方法首先使用深度先验初始化高斯点云，利用二值运动掩码识别具有显著深度变化的像素，并通过融合多个帧的深度图点云进行初始点云的生成。我们采用灵活的变形模型来表示动态场景，并引入归一化深度正则化损失和无监督深度平滑约束，以确保更加精确的几何重建。在两个真实手术数据集上的大量实验表明，SurgicalGS在几何精度方面达到了当前最先进的重建质量，推动了3D高斯散射在机器人辅助手术中的实用性。