ExtraGS: 拡散ガイドによる3Dガウススプラッティングを用いた内視鏡視野外挿の強化
ExtraGS: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting
内視鏡映像から3Dシーンを再構成し、視野外の解剖学的構造を拡散モデルで補完することで、内視鏡の視野外挿を高精度化するフレームワークを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Cheng-Tai Hsieh, Jiwei Shan, Han Fang, Jianshu Hu, Tao Ni, Lijun Han, Yutong Ban, Shing Shin Cheng, Hesheng Wang
分類: cs.CV
原文アブストラクト
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 the 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 view extrapolation through diffusion-guided 3D Gaussian Splatting. Starting from an initial reconstruction, we introduce an uncertainty-guided virtual camera sampling strategy to actively explore blind spots and maximize information gain. The rendered views from these sampled locations are refined using a diffusion model to recover plausible anatomical structures, producing pseudo-observations that guide further optimization. To prevent the generated content from degrading reliable regions, we adopt a confidence-weighted fine-tuning strategy when incorporating these pseudo-observations. Extensive experiments on multiple public endoscopic datasets demonstrate that ExtraGS significantly reduces extrapolation artifacts and achieves state-of-the-art performance in endoscopic novel view synthesis.
