How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey
It addresses the need for a fundamental reference on recent advancements in SLAM for researchers and practitioners, but it is incremental as it synthesizes existing work rather than presenting new findings.
This paper provides a comprehensive survey on how Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS) are reshaping Simultaneous Localization and Mapping (SLAM), highlighting the evolutionary path, strengths, and limitations of these methods.
Over the past two decades, research in the field of Simultaneous Localization and Mapping (SLAM) has undergone a significant evolution, highlighting its critical role in enabling autonomous exploration of unknown environments. This evolution ranges from hand-crafted methods, through the era of deep learning, to more recent developments focused on Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS) representations. Recognizing the growing body of research and the absence of a comprehensive survey on the topic, this paper aims to provide the first comprehensive overview of SLAM progress through the lens of the latest advancements in radiance fields. It sheds light on the background, evolutionary path, inherent strengths and limitations, and serves as a fundamental reference to highlight the dynamic progress and specific challenges.