diff --git a/README.md b/README.md
index f724f97..1c38c40 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,11 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/KTH-RPL/dufomap) · [上游 README](https://github.com/KTH-RPL/dufomap/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 <p>
-  <h1 align="center">DUFOMap: Efficient Dynamic Awareness Mapping</h1>
+  <h1 align="center">DUFOMap：高效的动态感知建图（Efficient Dynamic Awareness Mapping）</h1>
 </p>
 
 [![arXiv](https://img.shields.io/badge/arXiv-2403.01449-b31b1b?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2403.01449)
@@ -7,17 +13,17 @@
 [![poster](https://img.shields.io/badge/RAL2024|Poster-6495ed?style=flat&logo=Shotcut&logoColor=wihte)](https://mit-spark.github.io/Longterm-Perception-WS/assets/proceedings/DUFOMap/poster.pdf)
 [![video](https://img.shields.io/badge/video-YouTube-FF0000?logo=youtube&logoColor=white)](https://youtu.be/isDnAVoVD5M)
 
-Quick Demo: Run with the **same parameter setting** without tuning for different sensor (e.g 16, 32, 64, and 128 channel LiDAR and Livox-series mid360), the following shows the data collected from:
+快速演示：在不同传感器（例如 16、32、64 和 128 线 LiDAR 以及 Livox 系列 mid360）上使用**相同参数设置**运行，无需针对每种传感器调参。以下展示采集自以下设备的数据：
 
 | Leica-RTC360 | 128-channel LiDAR | Livox-mid360 |
 | ------- | ------- | ------- |
 | ![](assets/imgs/dufomap_leica.gif) | ![](assets/imgs/doals_train_128.gif) | ![](assets/imgs/two_floor_mid360.gif) |
 <!-- | ------- | ------- | ------- | -->
 
-🚀 2024-11-20: Update dufomap Python API from [SeFlow](https://github.com/KTH-RPL/SeFlow) try it now! `pip install dufomap` and run `python main.py --data_dir data/00` to get the cleaned map directly. Support all >=Python 3.8 in Windows and Linux. Please extract your own data to **the unified format** first follow [this wiki page](https://kth-rpl.github.io/DynamicMap_Benchmark/data/creation/#custom-data).
+🚀 2024-11-20：已从 [SeFlow](https://github.com/KTH-RPL/SeFlow) 更新 dufomap Python API，立即试用！运行 `pip install dufomap` 并执行 `python main.py --data_dir data/00` 即可直接获得清理后的地图。支持 Windows 和 Linux 上的所有 >=Python 3.8 版本。请先将你自己的数据提取为**统一格式**，并按照[此 wiki 页面](https://kth-rpl.github.io/DynamicMap_Benchmark/data/creation/#custom-data). 操作
 
 
-Clone quickly and init submodules:
+克隆仓库并初始化子模块：
 ```bash
 git clone --recursive -b main --single-branch https://github.com/KTH-RPL/dufomap.git
 
@@ -28,36 +34,36 @@ python main.py --data_dir data/00
 ```
 
 
-### Dependencies
+### 依赖项
 
-If you want to compile the C++ source version, please install the following dependencies:
+如需编译 C++ 源码版本，请安装以下依赖项：
 
 ```bash
 sudo apt update && sudo apt install gcc-10 g++-10
 sudo apt install libtbb-dev liblz4-dev
 ```
 
-Or you can directly build docker image through our [Dockerfile](Dockerfile):
+或者，你也可以通过我们的 [Dockerfile](Dockerfile) 直接构建 Docker 镜像：
 ```bash
 docker build -t dufomap .
 ```
 
-### 1. Build & Run
+### 1. 构建与运行
 
-Build:
+构建：
 
 ```bash
 cmake -B build -D CMAKE_CXX_COMPILER=g++-10 && cmake --build build
 ```
 
-Prepare Data: Teaser data (KITTI 00: 384.4Mb) can be downloaded via follow commands, more data detail can be found in the [dataset section](https://kth-rpl.github.io/DynamicMap_Benchmark/data) or format your own dataset follow [custom dataset section](https://kth-rpl.github.io/DynamicMap_Benchmark/data/creation/#custom-data).
+准备数据：可通过以下命令下载 Teaser 数据（KITTI 00：384.4Mb），更多数据详情见[数据集章节](https://kth-rpl.github.io/DynamicMap_Benchmark/data)，或按照[自定义数据集章节](https://kth-rpl.github.io/DynamicMap_Benchmark/data/creation/#custom-data). 格式化你自己的数据集
 
 ```bash
 wget https://zenodo.org/records/8160051/files/00.zip -p data
 unzip data/00.zip -d data
 ```
 
-Run:
+运行：
 
 ```bash
 ./build/dufomap_run data/00 assets/config.toml
@@ -65,27 +71,27 @@ Run:
 
 ![dufomap](assets/demo.png)
 
-## 2. Evaluation
+## 2. 评估
 
-Please reference to [DynamicMap_Benchmark](https://github.com/KTH-RPL/DynamicMap_Benchmark) for the evaluation of DUFOMap and comparison with other dynamic removal  methods.
+有关 DUFOMap 的评估及与其他动态物体移除方法的对比，请参考 [DynamicMap_Benchmark](https://github.com/KTH-RPL/DynamicMap_Benchmark)。
 
-[Evaluation Section link](https://github.com/KTH-RPL/DynamicMap_Benchmark/blob/master/scripts/README.md#evaluation)
+[评估章节链接](https://github.com/KTH-RPL/DynamicMap_Benchmark/blob/master/scripts/README.md#evaluation)
 
 
-## Acknowledgements
+## 致谢
 
-Thanks to HKUST Ramlab's members: Bowen Yang, Lu Gan, Mingkai Tang, and Yingbing Chen, who help collect additional datasets. 
+感谢香港科技大学 Ramlab 的成员：Bowen Yang、Lu Gan、Mingkai Tang 和 Yingbing Chen，他们帮助收集了额外数据集。
 
-This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program ([WASP](https://wasp-sweden.org/)) funded by the Knut and Alice Wallenberg Foundation including the WASP NEST PerCorSo.
+本工作部分由瓦伦堡 AI、自主系统与软件计划（[WASP](https://wasp-sweden.org/)) 资助，资助方包括 Knut and Alice Wallenberg Foundation 以及 WASP NEST PerCorSo。
 
-Feel free to explore below projects that use [ufomap](https://github.com/UnknownFreeOccupied/ufomap) (attach code links as follows):
+欢迎探索以下使用 [ufomap](https://github.com/UnknownFreeOccupied/ufomap) 的项目（代码链接如下）：
 - [RA-L'24 DUFOMap, Dynamic Awareness]()
 - [RA-L'23 SLICT, SLAM](https://github.com/brytsknguyen/slict)
 - [RA-L'20 UFOMap, Mapping Framework](https://github.com/UnknownFreeOccupied/ufomap)
 
-### Citation
+### 引用
 
-Please cite our works if you find these useful for your research.
+若你认为这些工作对你的研究有帮助，请引用我们的论文。
 
 ```
 @article{daniel2024dufomap,