V2X-Radar released

README.md

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 ---
+viewer: false
+annotations_creators:
+- expert-annotated
+language:
+- en
 license: cc-by-nc-nd-4.0
+multilinguality: monolingual
+pretty_name: V2X-Radar
+size_categories:
+- 10K<n<100K
+source_datasets: []
+tags:
+- autonomous-driving
+- cooperative-perception
+- multimodal
+- 4d-radar
+- v2x
+- lidar
+- camera
+
+task_categories:
+- robotics
+- object-detection
 ---
+
+<p align="center">
+  <h1 align="center">V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception</h1>
+  <p align="center">
+    <a href="https://scholar.google.com.hk/citations?user=EUnI2nMAAAAJ&hl=zh-CN&oi=sra"><strong>Lei Yang</strong></a>
+    · 
+    <a href="https://scholar.google.com.hk/citations?user=0Q7pN4cAAAAJ&hl=zh-CN"><strong>Xinyu Zhang</strong></a>
+    ·
+    <a href="https://www.tsinghua.edu.cn/"><strong>Jun Li</strong></a>
+    ·
+    <a href="https://www.tsinghua.edu.cn/"><strong>Chen Wang</strong></a>
+    ·
+    <a href="https://scholar.google.com.hk/citations?user=S3cQz1AAAAAJ&hl=zh-CN&oi=ao"><strong>Jiaqi Ma</strong></a>
+    ·
+    <a href="https://scholar.google.com.hk/citations?user=joReSgYAAAAJ&hl=zh-CN&oi=sra"><strong>Zhiying Song</strong></a>
+    ·
+    <a href="https://scholar.google.com.hk/citations?user=tTnWi_EAAAAJ&hl=zh-CN"><strong>Tong Zhao</strong></a>
+    ·
+    <a href="https://scholar.google.com.hk/citations?user=tIjCAKEAAAAJ&hl=zh-CN"><strong>Ziying Song</strong></a>
+    ·
+    <a href="https://scholar.google.com.hk/citations?user=pmzKjcUAAAAJ&hl=zh-CN"><strong>Li Wang</strong></a>
+    ·
+    <a href="https://www.tsinghua.edu.cn/"><strong> Mo Zhou</strong></a>
+    ·
+    <a href="https://www.tsinghua.edu.cn/"><strong> Yang Shen</strong></a>
+    ·
+    <a href="https://scholar.google.com.hk/citations?hl=zh-CN&user=ElfT3eoAAAAJ"><strong> Kai Wu</strong></a>
+    ·
+    <a href="https://scholar.google.com.hk/citations?user=UKVs2CEAAAAJ&hl=zh-CN"><strong> Chen Lv</strong></a>
+</p>
+
+<div align="center">
+  <img src="./assets/teaser-v2.jpg" alt="Logo" width="100%">
+</div>
+
+<p align="center">
+  <br>
+    <a href="https://neurips.cc/virtual/2025/poster/121426"><img alt="website" src="https://img.shields.io/badge/Website-Explore%20Now-blueviolet?style=flat&logo=google-chrome"></a>
+    <a href="https://arxiv.org/pdf/2411.10962"><img alt="paper" src="https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg"></a>
+    <a href="https://github.com/yanglei18/V2X-Radar">
+    <img alt="github" src="https://img.shields.io/badge/GitHub-Code-black?style=flat&logo=github"></a>
+    <a href='https://youtu.be/nzmj_-9M_lg'><img src='https://img.shields.io/badge/Video-Presentation-F9D371' alt='Docker'></a>
+
+  </a>
+    <br></br>
+    </a>
+  </p>
+</p>
+
+This is the official implementation of **"V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception"** (<span style="color:red">**NeuIPS 2025 Spotlight**</span>).
+
+Supported by the [THU OpenMDP Lab](http://openmpd.com/column/V2X-Radar).
+
+## 📘 Dataset Summary
+**V2X-Radar** is a large-scale cooperative perception dataset collected from complex urban intersections in mainland China. It is the **first public dataset** that integrates **4D imaging radar**, **LiDAR**, and **multi-view cameras** across **vehicle-to-everything (V2X)** configurations. The dataset aims to advance **multi-sensor fusion**, **cooperative 3D detection**, and **adverse-weather perception** research in autonomous driving.
+
+## 🧩 Supported Tasks
+- **3D Object Detection** (Radar/LiDAR/Camera/V2X Fusion)  
+- **Cooperative Perception** (V2V / V2I / V2X)  
+- **Temporal Misalignment & Communication Delay Benchmarking**  
+- **Domain Adaptation and Sensor-Robust Learning**
+
+## 🗣️ Languages
+All metadata and annotations are provided in **English**.  
+File paths and geographic identifiers are anonymized to comply with Chinese data export regulations.
+
+## 📊 Dataset Structure
+```
+V2X-Radar
+│   ├── V2X-Radar-I   # KITTI Format
+│   │   ├── training
+│   │   │   ├── velodyne
+│   │   │   ├── radar
+│   │   │   ├── calib
+│   │   │   ├── image_1
+│   │   │   ├── image_2
+│   │   │   ├── image_3
+│   │   │   ├── label_2
+│   │   ├── ImageSets
+│   │   │   ├── train.txt
+│   │   │   ├── trainval.txt
+│   │   │   ├── val.txt
+│   │   │   ├── test.txt
+│   ├── V2X-Radar-V   # KITTI Format
+│   │   ├── training
+│   │   │   ├── velodyne
+│   │   │   ├── radar 
+│   │   │   ├── calib
+│   │   │   ├── image_2
+│   │   │   ├── label_2
+│   │   ├── ImageSets
+│   │   │   ├── train.txt
+│   │   │   ├── trainval.txt
+│   │   │   ├── val.txt
+│   │   │   ├── test.txt
+│   ├── V2X-Radar-C  # OpenV2V Format
+│   │   ├── train
+│   │   │   ├── 2024-05-15-16-28-09
+│   │   │   │   ├── -1  # RoadSide
+│   │   │   │   │   ├── 00000.pcd - 00250.pcd # LiDAR point clouds from timestamp 0 to 250
+│   │   │   │   │   ├── 00000_radar.pcd - 00250_radar.pcd # the 4D Radar point clouds from timestamp 0 to 250
+│   │   │   │   │   ├── 00000.yaml - 00250.yaml # metadata for each timestamp
+│   │   │   │   │   ├── 00000_camera0.jpg - 00250_camera0.jpg # left camera images
+│   │   │   │   │   ├── 00000_camera1.jpg - 00250_camera1.jpg # front camera images
+│   │   │   │   │   ├── 00000_camera2.jpg - 00250_camera2.jpg # right camera images
+│   │   │   │   ├── 142 # Vehicle Side
+│   │   ├── validate
+│   │   ├── test
+```
+
+## ⚙️ Data Fields
+
+| Field | Type | Description |
+|:------|:----:|:------------|
+| `radar_points` | array(float) | 4D Radar point clouds (x, y, z, doppler, intensity) |
+| `lidar_points` | array(float) | LiDAR point clouds |
+| `images` | list(image) | Multi-view RGB frames |
+| `calibration` | dict | Intrinsics + extrinsics |
+| `timestamp` | float | Absolute timestamp (ms) |
+| `annotations` | dict | 3D bounding boxes, categories and track IDs |
+
+
+## 🧭 Data Collection and Geographic Coverage
+Data were recorded in **Chinese metropolitan cities** using research-licensed vehicles and roadside units.  
+All raw sensor data underwent **manual anonymization** and **privacy filtering** (no personal identities, license plates, or facial information remain).
+
+
+## ⚖️ Licensing Information
+This dataset is released under the **CC BY-NC-ND 4.0**.
+
+- **Attribution** — Users must credit “V2X-Radar Dataset, 2025”.  
+- **Non-Commercial** — Use for research and education only.  
+- **No Derivatives** — Do not redistribute modified versions.
+
+Full license text: [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)
+
+
+## 🪪 Citation
+```bibtex
+@article{yang2024v2x,
+  title={V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception},
+  author={Yang, Lei and Zhang, Xinyu and Li, Jun and Wang, Chen and Ma, Jiaqi and Song, Zhiying and Zhao, Tong and Song, Ziying and Wang, Li and Zhou, Mo and Shen, Yang and Lv, Chen},
+  journal={Advances in Neural Information Processing Systems (NeurIPS)},
+  year={2025}
+}

V2X-Radar-C/test/test.tar.gz

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V2X-Radar-C/train/train.tar.gz

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V2X-Radar-C/validate/validate.tar.gz

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V2X-Radar-I/ImageSets.zip

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V2X-Radar-V/ImageSets.zip

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