Endo-4DGS：基于4D高斯泼溅的内窥镜单目场景重建
Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting
AI总结：
本文提出Endo-4DGS，利用3D高斯泼溅、轻量级MLP形变场和Depth-Anything伪深度先验，结合置信度引导学习与深度正则化，实现内窥镜动态场景的实时高精度重建。
摘要：
在机器人辅助微创手术领域，动态场景重建能够显著增强下游任务并改善手术结果。基于神经辐射场（NeRF）的方法近年来因其卓越的场景重建能力而备受关注，但存在推理速度慢、训练时间长以及深度估计不一致等问题。一些先前的工作利用真实深度进行优化，但在手术领域难以获取。为克服这些障碍，我们提出了Endo-4DGS，一种实时内窥镜动态重建方法，利用3D高斯泼溅（GS）进行三维表示。具体而言，我们提出轻量级多层感知机（MLP），通过高斯形变场捕捉时间动态。为获得令人满意的高斯初始化，我们利用强大的深度估计基础模型Depth-Anything生成伪深度图作为几何先验。此外，我们提出置信度引导学习，以解决单目深度估计中的病态问题，并通过表面法线约束和深度正则化增强深度引导重建。我们的方法已在两个手术数据集上得到验证，能够实现实时渲染、高效计算以及高精度重建。
英文摘要：
In the realm of robot-assisted minimally invasive surgery, dynamic scene reconstruction can significantly enhance downstream tasks and improve surgical outcomes. Neural Radiance Fields (NeRF)-based methods have recently risen to prominence for their exceptional ability to reconstruct scenes but are hampered by slow inference speed, prolonged training, and inconsistent depth estimation. Some previous work utilizes ground truth depth for optimization but is hard to acquire in the surgical domain. To overcome these obstacles, we present Endo-4DGS, a real-time endoscopic dynamic reconstruction approach that utilizes 3D Gaussian Splatting (GS) for 3D representation. Specifically, we propose lightweight MLPs to capture temporal dynamics with Gaussian deformation fields. To obtain a satisfactory Gaussian Initialization, we exploit a powerful depth estimation foundation model, Depth-Anything, to generate pseudo-depth maps as a geometry prior. We additionally propose confidence-guided learning to tackle the ill-pose problems in monocular depth estimation and enhance the depth-guided reconstruction with surface normal constraints and depth regularization. Our approach has been validated on two surgical datasets, where it can effectively render in real-time, compute efficiently, and reconstruct with remarkable accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
