How NeRFs and 3D Gaussian Splatting areReshaping SLAM: a Survey

Abstract---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


TABLE 1: SLAM Systems Overview. We categorize the different methods into main RGB-D, RGB, and LiDAR-based
frameworks. In the leftmost column, we identify sub-categories of methods sharing specific properties, detailed in Sections
3.2.1 to 3.3.2 . Then, for each method, we report, from the second leftmost column to the second rightmost, the method name
and publication venue, followed by (a) the input modalities they can process: RGB, RGB-D, D ( e.g. LiDAR, ToF, Kinect,
etc.), stereo, IMU, or events; (b) mapping properties: scene encoding and geometry representations learned by the model;
(c) additional outputs learned by the method, such as object/semantic segmentation, or uncertainty modeling (Uncert.);
(d) tracking properties related to the adoption of a frame-to-frame or frame-to-model approach, the utilization of external
trackers, Global Bundle Adjustment (BA), or Loop Closure; (e) advanced design strategies, such as modeling sub-maps or
dealing with dynamic environments (Dyn. Env.); (f) the use of additional priors. Finally, we report the link to the project
page or source code in the rightmost column. indicates code not released yet

相关推荐
mit6.8249 分钟前
[nanoGPT] 编排训练 | `get_batch` | AdamW | `get_lr` | 分布式训练(DDP)
人工智能
rengang6611 分钟前
30-机器学习应用案例:展示机器学习在各行业中的典型应用实例
人工智能·机器学习
云烟成雨TD14 分钟前
NumPy 2.x 完全指南【四十二】线性代数之向量运算
python·机器学习·numpy
盈创力和200719 分钟前
以太网多参量传感器:超越温湿度的“智能嗅探”,守护每一方空气的安全
大数据·人工智能
wwlsm_zql35 分钟前
江西移动5G赋能:电力行业智能化革新探秘
人工智能·5g
迪三达42 分钟前
GPT-0: Attention+Transformer+可视化
gpt·深度学习·transformer
ChatPPT_YOO1 小时前
告别手搓PPT:实测四款免费AI生成工具
人工智能·信息可视化·powerpoint·ai生成ppt·ppt制作
caiyueloveclamp1 小时前
便宜好用AIPPT推荐TOP8【2025最新】
大数据·人工智能·powerpoint·ai生成ppt·aippt·免费会员
CHENKONG_CK1 小时前
RFID 技术赋能汽车制造:发动机气缸缸体生产线智能化升级案例
人工智能·生产制造·rfid
葡萄城技术团队2 小时前
实战视角:为何专用小型语言模型(SLM)正成为企业 AI 选型新宠—与 LLM 的全面对比指南
大数据·人工智能·语言模型