Neural fields, coordinate-based neural networks, have recently gained popularity for implicitly representing a scene. In contrast to classical methods that are based on explicit representations such as point clouds, neural fields provide a continuous scene representation able to represent 3D geometry and appearance in a way which is compact and ideal for robotics applications. However, limited prior methods have investigated registering multiple neural fields by directly utilising these continuous implicit representations. In this paper, we present Reg-NF, a neural fields-based registration that optimises for the relative 6-DoF transformation between two arbitrary neural fields, even if those two fields have different scale factors. Key components of Reg-NF include a bidirectional registration loss, multi-view surface sampling, and utilisation of volumetric signed distance functions (SDFs). We showcase our approach on a new neural field dataset for evaluating registration problems. We provide an exhaustive set of experiments and ablation studies to identify the performance of our approach, while also discussing limitations to provide future direction to the research community on open challenges in utilizing neural fields in unconstrained environments.

翻译：神经场（即基于坐标的神经网络）近期在隐式场景表征领域广受关注。与基于点云等显式表征的传统方法不同，神经场通过连续场景表征，以紧凑且适用于机器人应用的方式对三维几何与外观进行建模。然而，现有研究尚未充分探讨如何直接利用这类连续隐式表征实现多神经场间的配准。本文提出Reg-NF方法——一种基于神经场的配准算法，即使在两个神经场具有不同尺度因子的情况下，也能优化求解两者间的六自由度相对变换。该方法的核心组件包括双向配准损失函数、多视角表面采样策略以及体有符号距离函数（SDF）。我们构建了用于评估配准问题的新型神经场数据集，通过详尽的实验与消融研究验证了方法性能，同时讨论了当前技术局限，为科研社区在非约束环境中应用神经场的开放性挑战指明未来方向。