Monocular Endoscopic Tissue 3D Reconstruction with Multi-Level Geometry Regularization
This addresses a domain-specific problem for robot-assisted surgery by providing incremental improvements in tissue surface reconstruction and real-time rendering.
The paper tackles the problem of reconstructing deformable endoscopic tissues for robot-assisted surgery by introducing a novel approach based on 3D Gaussian Splatting with multi-level geometry regularization, achieving solid reconstruction quality in textures and geometries with fast rendering.
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.