Neural Radiance Field in Autonomous Driving: A Survey

TL;DR

This survey reviews the emerging applications of Neural Radiance Fields (NeRF) in autonomous driving, covering perception, 3D reconstruction, SLAM, and simulation, highlighting current progress and future research directions.

Contribution

It is the first comprehensive survey focusing on NeRF applications in autonomous driving, categorizing and analyzing existing methods and identifying future challenges.

Findings

NeRF enhances 3D perception and reconstruction in autonomous driving.

NeRF-based methods improve view synthesis and localization accuracy.

The survey identifies key research gaps and future directions in NeRF for AD.

Abstract

Neural Radiance Field (NeRF) has garnered significant attention from both academia and industry due to its intrinsic advantages, particularly its implicit representation and novel view synthesis capabilities. With the rapid advancements in deep learning, a multitude of methods have emerged to explore the potential applications of NeRF in the domain of Autonomous Driving (AD). However, a conspicuous void is apparent within the current literature. To bridge this gap, this paper conducts a comprehensive survey of NeRF's applications in the context of AD. Our survey is structured to categorize NeRF's applications in Autonomous Driving (AD), specifically encompassing perception, 3D reconstruction, simultaneous localization and mapping (SLAM), and simulation. We delve into in-depth analysis and summarize the findings for each application category, and conclude by providing insights and discussions on future directions in this field. We hope this paper serves as a comprehensive reference for researchers in this domain. To the best of our knowledge, this is the first survey specifically focused on the applications of NeRF in the Autonomous Driving domain.