Neural Fields have emerged as a transformative approach for 3D scene representation in computer vision and robotics, enabling accurate inference of geometry, 3D semantics, and dynamics from posed 2D data. Leveraging differentiable rendering, Neural Fields encompass both continuous implicit and explicit neural representations enabling high-fidelity 3D reconstruction, integration of multi-modal sensor data, and generation of novel viewpoints. This survey explores their applications in robotics, emphasizing their potential to enhance perception, planning, and control. Their compactness, memory efficiency, and differentiability, along with seamless integration with foundation and generative models, make them ideal for real-time applications, improving robot adaptability and decision-making. This paper provides a thorough review of Neural Fields in robotics, categorizing applications across various domains and evaluating their strengths and limitations, based on over 200 papers. First, we present four key Neural Fields frameworks: Occupancy Networks, Signed Distance Fields, Neural Radiance Fields, and Gaussian Splatting. Second, we detail Neural Fields' applications in five major robotics domains: pose estimation, manipulation, navigation, physics, and autonomous driving, highlighting key works and discussing takeaways and open challenges. Finally, we outline the current limitations of Neural Fields in robotics and propose promising directions for future research. Project page: https://robonerf.github.io

翻译：神经场已成为计算机视觉和机器人学中三维场景表示的一种变革性方法，能够从已配准的二维数据中精确推断几何、三维语义和动态信息。通过利用可微分渲染技术，神经场涵盖了连续隐式和显式神经表示，实现了高保真三维重建、多模态传感器数据融合以及新视点生成。本综述探讨了其在机器人学中的应用，重点阐述了其在增强感知、规划与控制方面的潜力。其紧凑性、内存高效性和可微性，以及与基础模型和生成模型的无缝集成，使其成为实时应用的理想选择，从而提升了机器人的适应性和决策能力。本文基于超过200篇文献，对机器人学中的神经场进行了全面回顾，对其在不同领域的应用进行了分类，并评估了其优势与局限。首先，我们介绍了四种关键的神经场框架：Occupancy Networks、Signed Distance Fields、Neural Radiance Fields 和 Gaussian Splatting。其次，我们详细阐述了神经场在五大机器人学主要领域的应用：姿态估计、操作、导航、物理仿真和自动驾驶，重点介绍了关键研究工作，并讨论了其启示与开放挑战。最后，我们概述了神经场在机器人学中当前的局限性，并提出了未来研究的有前景方向。项目页面：https://robonerf.github.io