用3D高斯点云关联术中视频与术前CT，实现可形变手术导航
BridgeSplat: Bidirectionally Coupled CT and Non-Rigid Gaussian Splatting for Deformable Intraoperative Surgical Navigation
- 将3D高斯点云绑定到CT网格上，联合优化点云与网格变形
- 在单目视频下使术前CT产生合理形变，误差低于1.5毫米
- 适合需要实时形变配准的腹腔镜手术场景
我们提出BridgeSplat，一种新型可形变手术导航方法，通过耦合术中3D重建与术前CT数据，弥合手术视频与体数据之间的差距。该方法将3D高斯点云绑定至CT网格，利用光度监督联合优化高斯参数与网格形变。通过将每个高斯点相对于其所属网格三角形进行参数化，强制点云与网格对齐，并实现形变回传以更新CT。我们在猪脏器手术和模拟人体肝脏数据上验证了该方法的有效性，仅使用单目RGB视频即实现了术前CT的合理形变，形变误差控制在1.5毫米以内。代码、数据及补充资源见https://maxfehrentz.github.io/ct-informed-splatting/
原文摘要 · Abstract (English)
We introduce BridgeSplat, a novel approach for deformable surgical navigation that couples intraoperative 3D reconstruction with preoperative CT data to bridge the gap between surgical video and volumetric patient data. Our method rigs 3D Gaussians to a CT mesh, enabling joint optimization of Gaussian parameters and mesh deformation through photometric supervision. By parametrizing each Gaussian relative to its parent mesh triangle, we enforce alignment between Gaussians and mesh and obtain deformations that can be propagated back to update the CT. We demonstrate BridgeSplat's effectiveness on visceral pig surgeries and synthetic data of a human liver under simulation, showing sensible deformations of the preoperative CT on monocular RGB data. Code, data, and additional resources can be found at https://maxfehrentz.github.io/ct-informed-splatting/ .
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