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EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting

Authors :
Zhu, Lingting
Wang, Zhao
Cui, Jiahao
Jin, Zhenchao
Lin, Guying
Yu, Lequan
Publication Year :
2024

Abstract

Surgical 3D reconstruction is a critical area of research in robotic surgery, with recent works adopting variants of dynamic radiance fields to achieve success in 3D reconstruction of deformable tissues from single-viewpoint videos. However, these methods often suffer from time-consuming optimization or inferior quality, limiting their adoption in downstream tasks. Inspired by 3D Gaussian Splatting, a recent trending 3D representation, we present EndoGS, applying Gaussian Splatting for deformable endoscopic tissue reconstruction. Specifically, our approach incorporates deformation fields to handle dynamic scenes, depth-guided supervision with spatial-temporal weight masks to optimize 3D targets with tool occlusion from a single viewpoint, and surface-aligned regularization terms to capture the much better geometry. As a result, EndoGS reconstructs and renders high-quality deformable endoscopic tissues from a single-viewpoint video, estimated depth maps, and labeled tool masks. Experiments on DaVinci robotic surgery videos demonstrate that EndoGS achieves superior rendering quality. Code is available at https://github.com/HKU-MedAI/EndoGS.<br />Comment: Accepted by Embodied AI and Robotics for HealTHcare of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI EARTH 2024). 11 pages, 4 figures

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2401.11535
Document Type :
Working Paper