ExtraGS: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting
Robot-assisted minimally invasive surgery (MIS) critically depends on reliable endoscopic perception for navigation and safety. However, conventional endoscopes provide only a limited field of view, leaving large portions of surrounding anatomy unobserved. Recent neural rendering approaches, such as Neural Radiance Fields and 3D Gaussian Splatting, enable novel view synthesis from endoscopic videos, but their reliance on sparse observations often leads to severe artifacts when extrapolating beyond the training trajectory.In this work, we propose ExtraGS, a framework for enhancing endoscopic vi
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- PossiblePossibly related (embedding) · 45%Artificial Intelligence (AI) Otoscopic Image Triaging - openPR.com →
- LinkedLinked via arxiv author · 85%Cheng-Tai Hsieh →
“ExtraGS: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting”
- LinkedLinked via arxiv author · 85%Jiwei Shan →
“ExtraGS: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting”
- LinkedLinked via arxiv author · 85%Jizhan Fang →
“ExtraGS: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting”
- LinkedLinked via arxiv author · 85%Jianshu Hu →
“ExtraGS: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting”
- LinkedLinked via arxiv author · 85%Tao Ning →
“ExtraGS: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting”
- LinkedLinked via arxiv author · 85%Lijun Han →
“ExtraGS: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting”
- LinkedLinked via arxiv author · 85%Yutong Ban →
“ExtraGS: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting”