EndoPrior-GS：基于联合纹理先验的动态内窥镜重建
EndoPrior-GS: Dynamic Endoscopic Reconstruction with a Joint Texture Prior
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中文总结 AI 辅助
针对动态内窥镜重建中3DGS受虚假几何和光照制约的问题，提出EndoPrior-GS，利用联合纹理先验引导初始化与密度控制，在EndoNeRF和SCARED上Flow Error降低27.7%和25.8%，保持实时渲染。
中文摘要 AI 辅助
动态内窥镜重建是机器人手术和计算机辅助干预的基础。虽然3D高斯泼溅（3DGS）实现了实时渲染，但其在可变形术中环境中的应用仍受到虚假几何和变化光照的制约。为解决这些限制，我们提出了EndoPrior-GS，一种新颖的流程，它显式地耦合了帧提取的视觉启发式信息和估计的深度图。EndoPrior-GS从工具过滤的有效组织掩膜、非镜面光度滤波器和解剖结构显著性中推导出联合纹理先验，生成一个概率图，用于指导原始点初始化和后续的密度控制。该先验进一步通过纹理感知项扩展到时间域，在训练过程中动态权衡成对原始点的贡献。我们在基准数据集EndoNeRF和SCARED上进行了广泛实验，结果表明，我们的方法EndoPrior-GS在代表性方法上将Flow Error分别降低了27.7%和25.8%，同时保持了有竞争力的渲染质量和实时渲染速度。我们的项目网站可通过此https URL访问。
英文摘要
Dynamic endoscopic reconstruction is fundamental to robotic surgery and computer-assisted interventions. While 3D Gaussian Splatting (3DGS) realises real-time rendering, its application to deformable intraoperative environments remains constrained by spurious geometry and varying illuminations. To address these limitations, we introduce EndoPrior-GS, a novel pipeline that explicitly couples frame-extracted vision heuristics and estimated depth maps. EndoPrior-GS derives a joint texture prior from a tool-filtered valid tissue mask, a non-specular photometric filter, and anatomical structural salience, yielding a probability map that guides primitive initialisation and subsequent density control. The prior is further extended to the temporal domain through a texture-aware term that dynamically weighs pairwise primitive contributions during training. We conduct extensive experiments on benchmark datasets EndoNeRF and SCARED, and the obtained results show that our method EndoPrior-GS reduces Flow Error by 27.7% and 25.8% over the representative approaches while preserving competitive rendering quality and real-time rendering speed. Our project website is available at https://jiaqi-huang-77.github.io/EndoPrior-GS/.
发表机构
- Newcastle University（纽卡斯尔大学）
机构由 AI 辅助整理，请以论文原文为准。
