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Benchmarking Neural Radiance Fields for Autonomous Robots: An Overview

Yuhang Ming, Xingrui Yang, Weihan Wang, Zheng Chen, Jinglun Feng, Yifan Xing, Guofeng Zhang

TL;DR

This survey delineates how Neural Radiance Fields can advance autonomous robotics by enhancing 3D reconstruction, segmentation, pose estimation, SLAM, planning, and interaction. It categorizes NeRF-based methods across rigid and deformable reconstruction, semantic segmentation, and various pose-estimation and SLAM paradigms, while benchmarking them on standard datasets and metrics. It also identifies gaps, notably in dynamic scenes, domain adaptation, and large-scale outdoor robotics, and highlights promising directions such as Gaussian Splatting, language-conditioned NeRF, and diffusion-based refinement. The work serves as a roadmap for researchers to select, benchmark, and extend NeRF approaches to real-world robotic tasks, accelerating deployment in complex environments.

Abstract

Neural Radiance Fields (NeRF) have emerged as a powerful paradigm for 3D scene representation, offering high-fidelity renderings and reconstructions from a set of sparse and unstructured sensor data. In the context of autonomous robotics, where perception and understanding of the environment are pivotal, NeRF holds immense promise for improving performance. In this paper, we present a comprehensive survey and analysis of the state-of-the-art techniques for utilizing NeRF to enhance the capabilities of autonomous robots. We especially focus on the perception, localization and navigation, and decision-making modules of autonomous robots and delve into tasks crucial for autonomous operation, including 3D reconstruction, segmentation, pose estimation, simultaneous localization and mapping (SLAM), navigation and planning, and interaction. Our survey meticulously benchmarks existing NeRF-based methods, providing insights into their strengths and limitations. Moreover, we explore promising avenues for future research and development in this domain. Notably, we discuss the integration of advanced techniques such as 3D Gaussian splatting (3DGS), large language models (LLM), and generative AIs, envisioning enhanced reconstruction efficiency, scene understanding, decision-making capabilities. This survey serves as a roadmap for researchers seeking to leverage NeRFs to empower autonomous robots, paving the way for innovative solutions that can navigate and interact seamlessly in complex environments.

Benchmarking Neural Radiance Fields for Autonomous Robots: An Overview

TL;DR

This survey delineates how Neural Radiance Fields can advance autonomous robotics by enhancing 3D reconstruction, segmentation, pose estimation, SLAM, planning, and interaction. It categorizes NeRF-based methods across rigid and deformable reconstruction, semantic segmentation, and various pose-estimation and SLAM paradigms, while benchmarking them on standard datasets and metrics. It also identifies gaps, notably in dynamic scenes, domain adaptation, and large-scale outdoor robotics, and highlights promising directions such as Gaussian Splatting, language-conditioned NeRF, and diffusion-based refinement. The work serves as a roadmap for researchers to select, benchmark, and extend NeRF approaches to real-world robotic tasks, accelerating deployment in complex environments.

Abstract

Neural Radiance Fields (NeRF) have emerged as a powerful paradigm for 3D scene representation, offering high-fidelity renderings and reconstructions from a set of sparse and unstructured sensor data. In the context of autonomous robotics, where perception and understanding of the environment are pivotal, NeRF holds immense promise for improving performance. In this paper, we present a comprehensive survey and analysis of the state-of-the-art techniques for utilizing NeRF to enhance the capabilities of autonomous robots. We especially focus on the perception, localization and navigation, and decision-making modules of autonomous robots and delve into tasks crucial for autonomous operation, including 3D reconstruction, segmentation, pose estimation, simultaneous localization and mapping (SLAM), navigation and planning, and interaction. Our survey meticulously benchmarks existing NeRF-based methods, providing insights into their strengths and limitations. Moreover, we explore promising avenues for future research and development in this domain. Notably, we discuss the integration of advanced techniques such as 3D Gaussian splatting (3DGS), large language models (LLM), and generative AIs, envisioning enhanced reconstruction efficiency, scene understanding, decision-making capabilities. This survey serves as a roadmap for researchers seeking to leverage NeRFs to empower autonomous robots, paving the way for innovative solutions that can navigate and interact seamlessly in complex environments.
Paper Structure (40 sections, 17 equations, 5 figures, 8 tables)

This paper contains 40 sections, 17 equations, 5 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Background
  3. NeRF Review
  4. Related survey
  5. 3D Reconstruction
  6. Rigid Reconstruction
  7. Object-Level
  8. Larger-Scene
  9. Sequential-Input
  10. Deformable Reconstruction
  11. Physics-based
  12. Deformation with dynamic sequence:
  13. Datasets and Evaluations
  14. Segmentation
  15. Segmentation Interpolation
  16. ...and 25 more sections

Figures (5)

  • Figure 1: The structure of NeRF for Autonomous Robots: 3D Reconstruction ($\S$\ref{['sec::recon']}), Segmentation ($\S$\ref{['sec::segmentation']}), Pose Estimation ($\S$\ref{['sec::pose']}), SLAM ($\S$\ref{['sec::slam']}), Planning and Navigation ($\S$\ref{['sec::plan-navi']}), and Interaction ($\S$\ref{['sec::interac']}).
  • Figure 2: Overview of NeRF. Figure modified from nerf-c
  • Figure 3: Overview of NeRF-based 3D reconstruction pipeline.
  • Figure 4: An overview of NeRF-based pose estimation pipeline.
  • Figure 5: Overview of NeRF-based SLAM pipeline.