Neural Fields in Robotics: A Survey
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
67207dbe3eeb25161bee0078
Muhammad Zubair Irshad
67207dbe3eeb25161bee0079
Mauro Comi
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2025-06-09T10:14:45.198Z
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/avatars/887d795e0e650a8cc67e66f552187a73.svg
Mauro Comi
user
MauroC
67207dbe3eeb25161bee007a
Yen-Chen Lin
67207dbe3eeb25161bee007b
Nick Heppert
67207dbe3eeb25161bee007c
Abhinav Valada
67207dbe3eeb25161bee007d
Rares Ambrus
67207dbe3eeb25161bee007e
Zsolt Kira
67207dbe3eeb25161bee007f
Jonathan Tremblay
Neural Fields in Robotics: A Survey
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
Neural Fields, including frameworks like Occupancy Networks and Neural Radiance Fields, enhance robotics by enabling accurate 3D scene representation, perception, and real-time decision-making across various applications.
Neural Fields differentiable rendering continuous implicit representations explicit neural representations 3D reconstruction multi-modal sensor data novel viewpoints Occupancy Networks Signed Distance Fields Neural Radiance Fields Gaussian Splatting pose estimation manipulation navigation physics autonomous driving real-time applications robot adaptability decision-making limitations future research