README_CN.md

NeRF-Based-SLAM-Incredible-Insights

项目简介

欢迎来到 “NeRF-Based-SLAM-Incredible-Insights” 仓库。 该项目旨在提供对各种基于 NeRF（神经辐射场）的 Slam（同时定位和建图）算法的全面见解。 如果您对基于 NeRF 的 Slam 算法充满热情并希望深入研究其功能和代码库，那么您来对地方了。

如果您发现此存储库有用，请考虑 引用 和 加星 此项目。 请随意与其他人分享这个项目！

目录

该仓库包含：

1. 详细文档介绍各种NeRF-Based Slam算法，阐明其基本原理和算法工作流程，见解和注解等内容详见在视觉SLAM见解和激光SLAM见解里的每个[Paper Insights]、[Code Notes]、[Tracking Insights]模块。
2. 对于选中的NeRF-Based Slam算法，给出代码注释以方便理解其代码实现，详见Co-SLAM_Scene_Representation_Noted 和 Co-SLAM_Tracking_Noted。
3. 更多视频解析链接如下。

视觉SLAM见解

• 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]

激光SLAM见解

• 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]

视频链接

• [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]

知识星球成员可免费观看视频

致谢

本项目来自于计算机视觉life的“Nerf Based SLAM算法学习小组”，贡献成员包括（不区分先后顺序）：

田宇博、刘权祥、史慧、汪寿安、万静怡、钟志德、徐扬、张一、陈安东、夏宁宁

引用

@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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