单目内窥镜组织三维重建与多级几何正则化
Monocular Endoscopic Tissue 3D Reconstruction with Multi-Level Geometry Regularization
AI总结：
本文提出基于3D高斯点的单目内窥镜组织三维重建方法，结合多级几何正则化实现平滑表面与实时渲染。
摘要：
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高斯点的重建方法在实现一致的组织表面重建方面面临挑战，而现有NeRF方法缺乏实时渲染能力。为实现平滑的可变形表面和实时渲染，我们提出了一种基于3D高斯点的新方法。具体而言，我们引入了表面感知重建，首先采用基于符号距离场的方法构建网格，随后利用该网格约束高斯点的重建过程。此外，为了确保生成物理上合理的变形，我们引入了局部刚性和全局非刚性限制，以指导高斯变形，以适应高度可变形的软内窥镜组织特性。基于3D高斯点，我们的方法实现了快速的渲染过程和光滑的表面外观。与替代方法的定量和定性分析表明，我们的方法在纹理和几何方面都实现了良好的重建质量。
英文摘要：
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.
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。
