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.
最近的神经渲染进展使得利用神经网络重建高质量的 4D 场景成为可能。尽管 4D 神经重建很流行，但这种表示的配准仍然是一项具有挑战性的任务，尤其是在外科规划和模拟中的动态场景配准。本文提出了一种用于动态外科神经场景配准的新策略。我们首先利用 4D 高斯点光源来有效表示外科场景并捕捉静态和动态场景。然后，我们提出了一种空间感知特征聚合方法，称为空间加权杂乱（Spatially Weight Cluttering，SWC），用于准确对齐外科场景中的特征，从而实现精确且逼真的外科模拟。最后，我们提出了一种新颖的可变形场景配准策略，用于配准两个动态场景。通过结合空间和时间信息进行对应匹配，我们的方法在隐式神经表示的现有配准方法中表现优越。该方法有望改善外科规划和培训，最终提高患者的治疗效果。