Surgical Gaussian Surfels: Highly Accurate Real-time Surgical Scene Rendering
Journal:
arXiv
Published Date:
Mar 6, 2025
Abstract
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 transforms anisotropic point primitives into
surface-aligned elliptical splats by constraining the scale component of the
Gaussian covariance matrix along the view-aligned axis. We predict accurate
surfel motion fields using a lightweight Multi-Layer Perceptron (MLP) 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
