NeRF-Based-SLAM-Incredible-Insights

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Project Overview

Welcome to the "NeRF-Based-SLAM-Incredible-Insights" repository. This project aims to provide comprehensive insights into various NeRF (Neural Radiance Fields) based Slam (Simultaneous Localization and Mapping) algorithms. If you're enthusiastic about NeRF-based Slam algorithms and wish to delve deep into their functionality and codebase, you're in the right place.

If you find this repository useful, please consider CITING and STARING this project. Feel free to share this project with others!

Contents

This repository encompasses:

1. Detailed documentation on a variety of NeRF-based Slam algorithms, elucidating their fundamental principles and algorithmic workflows, such as every [Paper Insights] and [Code Notes] and [Tracking Insights] in Visual SLAM Insights and Lidar SLAM Insights.
2. Code annotations for selected NeRF-based Slam algorithms to facilitate comprehension of their code implementation, such as Co-SLAM_Scene_Representation_Noted and Co-SLAM_Tracking_Noted.
3. More analysis videos links are displayed below in video link.

Visual SLAM Insights

• NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, ECCV, 2020. [Paper Insights] [Paper] [Tensorflow Code] [Webpage] [Video]
• NICE-SLAM: Neural Implicit Scalable Encoding for SLAM, CVPR, 2021. [Code Notes] [Tracking Insights] [Mapping Insights] [Paper] [Code] [Website]
• iMap: Implicit Mapping and Positioning in Real-Time, ICCV, 2021. [Paper Insights] [Paper] [Website] [Video]
• NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM, arXiv, 2023. [Paper Insights] [Paper] [Video]
• Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM, CVPR, 2023. [Mapping Insights] [Paper] [Website]
• NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields, arXiv, 2022. [Paper Insights] [Problem Record] [Paper] [Pytorch Code] [Video]
• vMAP: Vectorised Object Mapping for Neural Field SLAM, CVPR, 2023. [Paper Insights] [Paper] [Website] [Pytorch Code] [Video]
• RO-MAP: Real-Time Multi-Object Mapping with Neural Radiance Fields, RAL, 2023. [Paper Insights] [Paper] [Code] [Video]
• Neural Implicit Dense Semantic SLAM, arXiv, 2023. [Paper Insights] [Paper]

Lidar SLAM Insights

• Efficient Implicit Neural Reconstruction Using LiDAR, ICRA, 2023. [Paper Insights] [Paper] [Website] [Pytorch Code] [Video]
• NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping, ICCV, 2023. [Paper Insights] [Paper] [Code]

Video Link

• [第01讲 田宇博-NeRF开篇论文解读 NeRF]
• [第02讲 田宇博-第一个稠密的实时NeRF SLAM iMAP论文解读]
• [第03讲 刘权祥-NICE SLAM论文解读]
• [第04讲 NICER SLAM论文解读]
• [第05讲（上）-刘权祥-NICE SLAM代码解读：整体代码框架及运行：跟踪]
• [第05讲（下）-刘权祥-NICE SLAM代码解读：整体代码框架及运行：跟踪]
• [第06讲（上）-汪寿安-NICE SLAM代码解读]
• [第06讲（下）-汪寿安-NICE SLAM代码解读]
• [第07讲 NICE SLAM代码解读：建图]
• [第08讲 钟至德-Co-SLAM论文解读]
• [第09讲 徐扬-Co-SLAM 代码解读：tracking]
• [第10讲 Co-SLAM 代码解读：mapping]
• [第11讲 张一 Co-SLAM 代码解读：Scene representation]
• [第12讲（上）-汪寿安-基于LiDAR的NeRF-LOAM论文解读]
• [第12讲（中）-汪寿安-基于LiDAR的NeRF-LOAM论文解读]
• [第12讲（下）-汪寿安-基于LiDAR的NeRF-LOAM论文解读]
• [第13讲 张一 NeRF-SLAM 论文框架梳理]
• [第14-15讲 陈安东 NeRF-SLAM 运行配置经验]
• [第16讲-夏宁宁-物体级vMAP 论文解读]
• [第17讲-徐扬-语义Neural Implicit Dense Semantic SLAM 论文解读]
• [第18讲-夏宁宁-实时多物体RO-MAP 论文解读]
• [第19讲-基于LiDAR的Efficient Implicit Neural Reconstruction Using LiDAR 论文解读]
• [第20讲-LiDAR全局定位 IRMCL Implicit Representation-based Online Global Localization论文解读]

zsxq members have video viewing rights

Acknowledgments

This project comes from the "Nerf Based SLAM Algorithm Learning Group" of CVLIFE. The contributing members include (in no particular order):

Tian Yubo, Liu Quanxiang, Shi Hui, Wang Shouan, Wan Jingyi, Zhong Zhide, Xu Yang, Zhang Yi, Chen Andong, Xia Ningning

Citation

@misc{electron2023nerfbasedslamincredibleinsights,
    title = {NeRF-Based-SLAM-Incredible-Insights},
    author = {electron6,shuttworth},
    journal = {GitHub repository},
    url = {https://github.com/electech6/NeRF-Based-SLAM-Incredible-Insights},
    year = {2023}
}

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