SurgicalGS: dynamic 3D Gaussian splatting for accurate robotic-assisted surgical scene reconstruction
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Accurate 3D reconstruction of dynamic surgical scenes from endoscopic video is essential for robotic-assisted surgery. While recent 3D Gaussian Splatting methods have shown promise in achieving high-quality reconstructions with fast rendering speeds, their use of inverse depth loss functions compresses depth variations. This can lead to a loss of fine geometric details, limiting their ability to capture precise 3D geometry and effectiveness in intraoperative applications. To address the limitations of existing methods, we developed SurgicalGS, a dynamic 3D Gaussian Splatting framework specifically designed for improved geometric accuracy in surgical scene reconstruction. Our approach integrates a temporally coherent multi-frame depth fusion and an adaptive motion mask for Gaussian initialisation. Besides, we represent dynamic scenes using the Flexible Deformation Model and introduce a novel normalized depth regularization loss and an unsupervised depth smoothness constraint to ensure high geometric accuracy in the reconstruction. Extensive experiments on two real surgical datasets demonstrate that SurgicalGS achieves state-of-the-art reconstruction quality, especially in precise geometry, advancing the usability of 3D Gaussian Splatting in robotic-assisted surgery. Our code is available at https://github.com/neneyork/SurgicalGS.
Date Issued
2026-01-01
Date Acceptance
2025-09-01
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2026, 15970, pp.572-582
ISBN
9783032051400
ISSN
0302-9743
Publisher
Springer
Start Page
572
End Page
582
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
15970
Copyright Statement
© 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
MICCAI 2025
Publication Status
Published
Start Date
2025-09-23
Finish Date
2025-09-27
Coverage Spatial
Daejeon, South Korea
Date Publish Online
2025-09-20