English

Monocular Endoscopic Tissue 3D Reconstruction with Multi-Level Geometry Regularization

Computer Vision and Pattern Recognition 2026-02-25 v1

Abstract

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.

Keywords

Cite

@article{arxiv.2602.20718,
  title  = {Monocular Endoscopic Tissue 3D Reconstruction with Multi-Level Geometry Regularization},
  author = {Yangsen Chen and Hao Wang},
  journal= {arXiv preprint arXiv:2602.20718},
  year   = {2026}
}

Comments

ijcnn 2025

R2 v1 2026-07-01T10:49:37.536Z