title: Neural Fields in Robotics: A Survey
summary: 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
paper_authors._id: 67207dbe3eeb25161bee0078
paper_authors.name: Muhammad Zubair Irshad
paper_authors._id: 67207dbe3eeb25161bee0079
paper_authors.name: Mauro Comi
paper_authors._id: 67207dbe3eeb25161bee007a
paper_authors.name: Yen-Chen Lin
paper_authors._id: 67207dbe3eeb25161bee007b
paper_authors.name: Nick Heppert
paper_authors._id: 67207dbe3eeb25161bee007c
paper_authors.name: Abhinav Valada
paper_authors._id: 67207dbe3eeb25161bee007d
paper_authors.name: Rares Ambrus
paper_authors._id: 67207dbe3eeb25161bee007e
paper_authors.name: Zsolt Kira
paper_authors._id: 67207dbe3eeb25161bee007f
paper_authors.name: Jonathan Tremblay
paper_title: Neural Fields in Robotics: A Survey
paper_summary: 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
paper_ai_keywords: Neural Fields differentiable rendering continuous implicit neural representations explicit neural representations high-fidelity 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 foundation models generative models real-time applications adaptability decision-making