Surgical Gaussian Surfels: Highly Accurate Real-time Surgical Scene Rendering (2503.04079v1)

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

Surgical Gaussian Surfels: Highly Accurate Real-time Surgical Scene Rendering

The paper "Surgical Gaussian Surfels: Highly Accurate Real-time Surgical Scene Rendering" proposes a novel method for rendering surgical scenes in real-time with high accuracy. The introduction of Surgical Gaussian Surfels (SGS) builds upon the challenges faced by existing methods, particularly in the dynamic and complex environments of robot-assisted minimally invasive surgery.

Summary of Contributions

The key contribution of this paper is the introduction of Surgical Gaussian Surfels (SGS), which transform traditional 3D Gaussian primitives into surface-aligned elliptical splats, thereby addressing the limitations associated with unrestricted Gaussian scaling and the lack of surface alignment constraints. This approach utilizes a constrained covariance matrix for each surfel primitive, enhancing the alignment with anatomical surfaces and improving accuracy in the geometric reconstruction of deformable tissues.

Additionally, the authors propose using a lightweight Multi-Layer Perceptron (MLP) to predict surfel motion fields, which is crucial for handling the complex tissue deformations observed in endoscopic videos. By splitting Gaussian Surfels in over-reconstructed regions and defining surface normals as the direction of the steepest density change within each primitive, the method circumvents the need for monocular normal priors. This approach significantly enhances the rendering efficiency, normal map quality, and surface geometry representation, as evaluated on two in-vivo surgical datasets.

Methodology

The SGS approach introduces several innovations in the area of surgical scene reconstruction:

• Surface-aligned Gaussian Surfels: These are constructed by setting the z-scale of Gaussian primitives to zero, resulting in elliptical splats that align better with tissue surfaces. This adjustment is instrumental in effectively capturing fine anatomical details.
• Projection-Based Iterative Mask Integration (PIMI): This initialization approach enhances the depth and color mapping of point clouds by leveraging confidence-weighted depth aggregation, which is crucial for resolving gaps and inaccuracies in the representation due to incomplete depth data.
• Optimization Strategy: The optimization of Surgical Gaussian Surfels involves a composite loss function that integrates photometric, total variation, and perceptual loss components, among others. This ensures geometric consistency, normal alignment, and artifact-free tissue rendering, even in occluded regions.

Quantitative and Qualitative Results

The experimental results detailed in the paper demonstrate the superior performance of the SGS method over existing techniques such as EndoNeRF, EndoSurf, and SurgicalGaussian. The proposed method achieves state-of-the-art results across commonly used image quality metrics like LPIPS, PSNR, and SSIM, showcasing its ability to render intricate surgical scenes with high fidelity. The data presented indicates a significant improvement in rendering efficiency and memory usage, positioning SGS as a highly efficient method suitable for real-time applications.

Implications and Future Directions

The advancements introduced by the SGS method have significant implications for the field of medical robotics and surgical simulation. By enhancing the accuracy and detail of rendered surgical scenes, SGS supports improved surgical instrument manipulation and offers a robust foundation for the development of AR/VR applications in medical training and simulation.

Future research could explore expanding the capabilities of SGS by integrating more sophisticated models for tissue deformation and incorporating real-time feedback from surgical instruments. Such advancements would further bridge the gap between simulated and real-world surgical environments, enabling more precise and reliable robot-assisted surgical procedures.

In conclusion, this paper presents a substantial contribution to the domain of surgical scene rendering, driven by innovative Gaussian surfel techniques that significantly enhance the geometric accuracy and real-time rendering quality in surgical contexts.