提升内窥镜手术动态场景重建精度与连贯性
Endo-NeRF++: Uncertainty-Aware Neural Rendering with Multi-Resolution Hash Encoding for Dynamic Surgical Scene Reconstruction
- 采用多分辨率哈希编码与时间特征融合，捕捉解剖细节并稳定变形场景
- 不确定性引导自适应采样使PSNR提升1.22dB，SSIM增5.3%，LPIPS降55.1%
- 适用于机器人辅助微创手术中复杂形变与遮挡场景的高保真渲染
动态手术场景重建对机器人辅助微创手术至关重要，但受限于组织形变、遮挡、镜面反射及视角受限，仍具挑战。本文提出Endo-NeRF++，一种考虑重建不确定性的神经渲染框架。在EndoNeRF基础上，引入多分辨率哈希网格编码、时间特征融合与不确定性驱动的自适应采样策略，以提升可变形内窥镜场景下的重建精度与时间连贯性。多分辨率哈希表示能有效捕获粗粒度与细粒度解剖结构，时间特征融合确保在组织形变和器械遮挡下保持稳定重建。不确定性引导的自适应采样将更多采样点分配至不确定区域，提升渲染质量与几何一致性。在机器人手术视频序列上的实验表明，该方法相比EndoNeRF基线，PSNR最高提升1.22dB（4.3%），SSIM提升5.3%，LPIPS降低55.1%。
原文摘要 · Abstract (English)
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.
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