Neural fields provide a continuous scene representation of 3D geometry and appearance in a way which has great promise for robotics applications. One functionality that unlocks unique use-cases for neural fields in robotics is object 6-DoF registration. In this paper, we provide an expanded analysis of the recent Reg-NF neural field registration method and its use-cases within a robotics context. We showcase the scenario of determining the 6-DoF pose of known objects within a scene using scene and object neural field models. We show how this may be used to better represent objects within imperfectly modelled scenes and generate new scenes by substituting object neural field models into the scene.

翻译：神经场以连续场景表示的方式描述三维几何与外观，在机器人领域具有广阔应用前景。其中，物体六自由度配准功能为神经场在机器人领域的应用提供了独特场景。本文对近期提出的Reg-NF神经场配准方法及其在机器人领域的应用场景进行了深入分析，重点展示了如何利用场景与物体神经场模型确定场景中已知物体的六自由度位姿，并阐明该方法如何更优地表示非完美建模场景中的物体，以及通过将物体神经场模型替换至场景中生成新场景的方式。