Registering Neural 4D Gaussians for Endoscopic Surgery

TL;DR

This paper introduces a novel neural scene registration method for 4D surgical scenes using Gaussian Splatting and spatially aware feature aggregation, improving accuracy in dynamic scene alignment for surgical applications.

Contribution

It proposes a new strategy combining 4D Gaussian Splatting and SWC for precise registration of dynamic surgical scenes, enhancing current neural registration techniques.

Findings

Achieves superior registration accuracy over existing methods.

Effectively captures both static and dynamic scene features.

Improves surgical planning and simulation outcomes.

Abstract

The recent advance in neural rendering has enabled the ability to reconstruct high-quality 4D scenes using neural networks. Although 4D neural reconstruction is popular, registration for such representations remains a challenging task, especially for dynamic scene registration in surgical planning and simulation. In this paper, we propose a novel strategy for dynamic surgical neural scene registration. We first utilize 4D Gaussian Splatting to represent the surgical scene and capture both static and dynamic scenes effectively. Then, a spatial aware feature aggregation method, Spatially Weight Cluttering (SWC) is proposed to accurately align the feature between surgical scenes, enabling precise and realistic surgical simulations. Lastly, we present a novel strategy of deformable scene registration to register two dynamic scenes. By incorporating both spatial and temporal information for correspondence matching, our approach achieves superior performance compared to existing registration methods for implicit neural representation. The proposed method has the potential to improve surgical planning and training, ultimately leading to better patient outcomes.