用表面贴片技术提升手术场景实时渲染精度，解决器械遮挡与细节丢失问题。
Surgical Gaussian Surfels: Highly Accurate Real-time Surgical Scene Rendering using Gaussian Surfels
- 将高斯点转为表面对齐椭圆贴片，约束尺度避免失真
- 新模型比标准MLP快5倍，精准追踪组织形变
- 无需深度先验即可准确估计表面法向，适合手术视觉系统
单目内窥镜视频中可变形组织的精确几何重建仍是机器人辅助微创手术中的核心挑战。尽管基于神经辐射场（NeRF）和3D高斯原始方法的体积化与点基方法能高效渲染手术场景，但在处理无伪影的器械遮挡和保持精细解剖细节方面仍存在不足。这主要源于高斯尺度无约束及重建过程中的表面对齐不足。为此，我们提出外科高斯贴片（Surgical Gaussian Surfels, SGS），通过限制高斯协方差矩阵在视图对齐轴上的尺度分量，将各向异性点原始转换为表面对齐的椭圆贴片。同时引入轻量级全融合形变多层感知机（FFD-MLP），其预测贴片运动场的速度比标准MLP快5倍，并结合局部性约束以应对复杂组织形变。通过同向视图空间位置梯度，在过重建区域分裂高斯贴片以捕捉精细图像细节。此外，定义表面法向为每个高斯贴片内密度变化最陡方向，实现无需单目法向先验的准确法向估计。我们在两个在体手术数据集上评估该方法，在表面几何、法向图质量与渲染效率方面超越现有最优方法，同时保持实时渲染性能竞争力。代码已开源：https://github.com/aloma85/SurgicalGaussianSurfels
原文摘要 · Abstract (English)
Accurate geometric reconstruction of deformable tissues in monocular endoscopic video remains a fundamental challenge in robot-assisted minimally invasive surgery. Although recent volumetric and point primitive methods based on neural radiance fields (NeRF) and 3D Gaussian primitives have efficiently rendered surgical scenes, they still struggle with handling artifact-free tool occlusions and preserving fine anatomical details. These limitations stem from unrestricted Gaussian scaling and insufficient surface alignment constraints during reconstruction. To address these issues, we introduce Surgical Gaussian Surfels (SGS), which transform anisotropic point primitives into surface-aligned elliptical splats by constraining the scale component of the Gaussian covariance matrix along the view-aligned axis. We also introduce the Fully Fused Deformation Multilayer Perceptron (FFD-MLP), a lightweight Multi-Layer Perceptron (MLP) that predicts accurate surfel motion fields up to 5x faster than a standard MLP. This is coupled with locality constraints to handle complex tissue deformations. We use homodirectional view-space positional gradients to capture fine image details by splitting Gaussian Surfels in over-reconstructed regions. In addition, we define surface normals as the direction of the steepest density change within each Gaussian surfel primitive, enabling accurate normal estimation without requiring monocular normal priors. We evaluate our method on two in-vivo surgical datasets, where it outperforms current state-of-the-art methods in surface geometry, normal map quality, and rendering efficiency, while remaining competitive in real-time rendering performance. We make our code available at https://github.com/aloma85/SurgicalGaussianSurfels
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