Neural Fields in Robotics: A Survey
Authors: name: Muhammad Zubair Irshad, name: Mauro Comi, name: Yen-Chen Lin, name: Nick Heppert, name: Abhinav Valada, name: Rares Ambrus, name: Zsolt Kira, name: Jonathan Tremblay

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