用多层级几何约束提升内窥镜软组织3D重建质量与实时性
Monocular Endoscopic Tissue 3D Reconstruction with Multi-Level Geometry Regularization
- 基于符号距离场构建网格，约束高斯点云重建过程
- 引入局部刚性与全局非刚性约束，实现物理合理变形
- 兼顾实时渲染与光滑表面，适合手术机器人应用
重建可变形内窥镜组织对于实现机器人辅助手术至关重要。然而，基于3D高斯泼溅的方法在保持一致的组织表面重建方面存在挑战，而现有NeRF方法缺乏实时渲染能力。为同时实现平滑可变形表面与实时渲染，本文提出一种基于3D高斯泼溅的新方法。首先采用基于符号距离场（SDF）的方法构建初始网格，再利用该网格约束高斯泼溅的重建过程。此外，为确保生成物理上合理的形变，引入局部刚性与全局非刚性约束，以适应软组织的高度可变形特性。所提方法在3D高斯泼溅基础上实现了快速渲染与平滑表面表现。定量与定性分析表明，相比其他方法，本方法在纹理和几何重建质量上均表现出色。
原文摘要 · Abstract (English)
Reconstructing deformable endoscopic tissues is crucial for achieving robot-assisted surgery. However, 3D Gaussian Splatting-based approaches encounter challenges in achieving consistent tissue surface reconstruction, while existing NeRF-based methods lack real-time rendering capabilities. In pursuit of both smooth deformable surfaces and real-time rendering, we introduce a novel approach based on 3D Gaussian Splatting. Specifically, we introduce surface-aware reconstruction, initially employing a Sign Distance Field-based method to construct a mesh, subsequently utilizing this mesh to constrain the Gaussian Splatting reconstruction process. Furthermore, to ensure the generation of physically plausible deformations, we incorporate local rigidity and global non-rigidity restrictions to guide Gaussian deformation, tailored for the highly deformable nature of soft endoscopic tissue. Based on 3D Gaussian Splatting, our proposed method delivers a fast rendering process and smooth surface appearances. Quantitative and qualitative analysis against alternative methodologies shows that our approach achieves solid reconstruction quality in both textures and geometries.
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