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.

本文介绍了神经场在机器人应用中对 3D 几何和外观的连续场景表示，重点探讨了神经场在机器人领域中用于对象 6 自由度注册的方法和应用，展示了利用场景和对象神经场模型确定已知对象在场景中的 6 自由度姿态的情景，并展示了如何在不完美模型的场景中更好地表示对象，并通过将对象神经场模型替换到场景中生成新场景的方法。

神经场中的物体注册

Object Registration in Neural Fields

When deploying a robot to a new task, one often has to train it to detect
novel objects, which is time-consuming and labor-intensive. We present TAILOR
-- a method and system for object registration with active and incremental
learning. When instructed by a human teacher to register an object, TAILOR is
able to automatically select viewpoints to capture informative images by
actively exploring viewpoints, and employs a fast incremental learning
algorithm to learn new objects without potential forgetting of previously
learned objects. We demonstrate the effectiveness of our method with a KUKA
robot to learn novel objects used in a real-world gearbox assembly task through
natural interactions.

TAILOR 是一种物体注册方法和系统，可通过活动和增量学习方式进行对象注册，有效地提高了机器人的学习效率。我们使用一台 KUKA 机器人，在实际齿轮箱装配任务中通过自然交互学习来证明我们方法的有效性。