Endo-NeRF++：面向动态手术场景重建的不确定性感知神经辐射场，采用多分辨率哈希编码

中文摘要 AI 辅助

重建动态手术场景对机器人辅助微创手术至关重要，但因组织变形、遮挡、镜面反射和受限视点，该任务仍具挑战性。本研究提出Endo-NeRF++，一种用于动态手术场景重建的不确定性感知神经辐射场框架，在EndoNeRF基础上扩展，整合多分辨率哈希网格编码、时间特征融合及不确定性引导的自适应采样，以提升可变形内窥镜场景的重建精度与时间一致性。框架内的多分辨率哈希网格表示可有效捕捉粗、细粒度解剖细节，时间特征融合确保组织变形及手术工具遮挡时的稳定重建；不确定性驱动的自适应采样则向不确定区域分配更多样本，以提升渲染质量与几何一致性。针对机器人手术视频序列的实验表明，与EndoNeRF基线相比，所提不确定性引导自适应采样使PSNR最高提升1.22 dB（4.3%），SSIM最高提升5.3%，LPIPS最高降低55.1%。

英文摘要

Reconstructing dynamic surgical scenes is crucial for robot-assisted minimally invasive surgery; however, it continues to be difficult because of tissue deformation, occlusions, specular reflections, and restricted viewpoints. In this study, we introduce Endo-NeRF++, a neural rendering framework that accounts for uncertainty in the reconstruction of dynamic surgical scenes. Expanding on EndoNeRF, the suggested approach incorporates multi-resolution hash-grid encoding, temporal feature merging, and uncertainty-informed adaptive sampling to enhance reconstruction accuracy and temporal coherence in deformable endoscopic scenes.The multi-resolution hash-grid representation within the framework effectively captures both coarse and fine anatomical details, while temporal feature blending ensures stable reconstruction during tissue deformation and surgical tool occlusions. Additionally, uncertainty-driven adaptive sampling assigns more samples to uncertain areas to enhance rendering quality and geometric coherence. Experiments on robotic surgical video sequences demonstrate that the proposed uncertainty-guided adaptive sampling improves PSNR by up to 1.22dB (4.3%), increases SSIM by up to 5.3%, and reduces LPIPS by up to 55.1% compared with the EndoNeRF baseline.

发表机构

• Finnish Center for Artificial Intelligence (FCAI)（芬兰人工智能中心）
• ELLIS Unit Helsinki（欧洲学习与智能系统研究所赫尔辛基分部）
• University of Helsinki（赫尔辛基大学）
• Institute for Urban Studies (Urbaria)（城市研究所）
• Institute of Sustainability Science (HELSUS)（可持续发展科学研究所）

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