ExtraGS: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting

2026-07-14 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

ExtraGS is a novel framework designed to enhance endoscopic view extrapolation, specifically for robot-assisted minimally invasive surgery (MIS), by employing diffusion-guided 3D Gaussian Splatting. Traditional endoscopes offer a limited field of view, and existing neural rendering techniques like Neural Radiance Fields and 3D Gaussian Splatting often produce severe artifacts when extrapolating beyond observed trajectories due to sparse data. ExtraGS addresses this by implementing an uncertainty-guided virtual camera sampling strategy to actively explore unobserved areas and maximize information gain. It then refines these rendered views using a diffusion model to reconstruct plausible anatomical structures, generating pseudo observations. These pseudo observations guide further optimization through a confidence-weighted fine-tuning strategy, preventing degradation of reliable regions. Experiments on multiple public endoscopic datasets demonstrate ExtraGS significantly reduces extrapolation artifacts and achieves leading performance in endoscopic novel view synthesis.

Key takeaway

For Computer Vision Engineers developing medical imaging systems or AI Scientists working on robot-assisted surgery, ExtraGS offers a robust solution to a critical perception challenge. If you are struggling with extrapolation artifacts from limited endoscopic fields of view, consider integrating diffusion-guided 3D Gaussian Splatting with uncertainty sampling. This approach significantly enhances novel view synthesis, providing more reliable anatomical context for navigation and safety in minimally invasive procedures.

Key insights

ExtraGS enhances endoscopic view extrapolation by integrating diffusion-guided 3D Gaussian Splatting with uncertainty-aware sampling.

Principles

• Actively explore blind spots with uncertainty-guided sampling.
• Refine rendered views using diffusion models for anatomical plausibility.
• Integrate generated content via confidence-weighted fine-tuning.

Method

Start with an initial 3D Gaussian Splatting reconstruction, then use uncertainty-guided virtual camera sampling to explore blind spots. Refine rendered views with a diffusion model to create pseudo observations, which guide optimization via confidence-weighted fine-tuning.

In practice

• Improve novel view synthesis for robot-assisted surgery.
• Reduce artifacts in extrapolated medical imaging.

Topics

• Endoscopic View Extrapolation
• 3D Gaussian Splatting
• Diffusion Models
• Robot-assisted Surgery
• Novel View Synthesis
• Medical Imaging

Best for: AI Scientist, Computer Vision Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.