NeRFs in Robotics: A Survey

TL;DR

This survey reviews the recent advances and applications of Neural Radiance Fields (NeRFs) in robotics, highlighting their advantages, limitations, and future potential for perception and interaction tasks.

Contribution

It provides a comprehensive overview of NeRFs in robotics, analyzing current applications, recent improvements, and future research directions in this emerging field.

Findings

NeRFs enable realistic 3D scene representations with low memory use.

Applications of NeRFs in robotics include perception and interaction tasks.

Future research aims to address current limitations and expand NeRF capabilities.

Abstract

Detailed and realistic 3D environment representations have been a long-standing goal in the fields of computer vision and robotics. The recent emergence of neural implicit representations has introduced significant advances to these domains, enabling numerous novel capabilities. Among these, Neural Radiance Fields (NeRFs) have gained considerable attention because of their considerable representational advantages, such as simplified mathematical models, low memory footprint, and continuous scene representations. In addition to computer vision, NeRFs have demonstrated significant potential in robotics. Thus, we present this survey to provide a comprehensive understanding of NeRFs in the field of robotics. By exploring the advantages and limitations of NeRF as well as its current applications and future potential, we aim to provide an overview of this promising area of research. Our survey is divided into two main sections: \textit{Applications of NeRFs in Robotics} and \textit{Advances for NeRFs in Robotics}, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in robotics for perception and interaction tasks. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRFs in robotics. In the discussion section of the review, we summarize the existing challenges and provide valuable future research directions.