EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting

• URL: http://arxiv.org/abs/2401.11535v3
• Date: Tue, 23 Jul 2024 07:47:13 GMT
• Title: EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting
• Authors: Lingting Zhu, Zhao Wang, Jiahao Cui, Zhenchao Jin, Guying Lin, Lequan Yu,
• Abstract summary: We present EndoGS, applying Gaussian Splatting for deformable endoscopic tissue reconstruction. Our approach incorporates deformation fields to handle dynamic scenes, depth-guided supervision with spatial-temporal weight masks, and surface-aligned regularization terms. As a result, EndoGS reconstructs and renders high-quality deformable endoscopic tissues from a single-viewpoint video, estimated depth maps, and labeled tool masks.
• Score: 20.848027172010358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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