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提供三维几何与外观描述，在机器人应用领域展现出巨大潜力。能够解锁神经场在机器人领域中独特应用场景的功能之一是物体六自由度配准。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扩展分析。我们展示了利用场景与物体神经场模型，确定场景中已知物体六自由度姿态的应用场景，并阐述了如何通过该方法在不完美建模的场景中更准确地表征物体，以及通过将物体神经场模型替换至场景中来生成新场景的技术路径。