Registering Neural 4D Gaussians for Endoscopic Surgery
Journal:
arXiv
Published Date:
Jul 29, 2024
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.
