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license: cc-by-nc-sa-4.0
size_categories:
- 1K<n<10K
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# RT-Pose: A 4D Radar Tensor-based 3D Human Pose Estimation and Localization Benchmark (ECCV 2024)

RT-Pose introduces a human pose estimation (HPE) dataset and benchmark by integrating a unique combination of calibrated 4D radar tensors, RGB images, and LiDAR point clouds. 
This integration marks a significant advancement in studying human pose analysis through multi-modality datasets.

![images](./asset/data_viz.gif)
![images](./asset/annotation.gif)


## Dataset Details

### Dataset Description

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#### Sensors
The data collection hardware system comprises two RGB [cameras](https://www.flir.com/products/blackfly-s-usb3/?model=BFS-U3-16S2C-CS), a non-repetitive
horizontal scanning [LiDAR](https://www.livoxtech.com/3296f540ecf5458a8829e01cf429798e/assets/horizon/Livox%20Horizon%20user%20manual%20v1.0.pdf), and a cascade imaging [radar module](https://www.ti.com/tool/MMWCAS-RF-EVM).
![images](./asset/device.png)


#### Data Statics
We collect the dataset in 40 scenes with indoor and outdoor environments.
![images](./asset/examples.png)


The dataset comprises 72,000 frames distributed across 240 sequences. 
The structured organization ensures a realistic distribution of human motions, which is crucial for robust analysis and model training.

![images](./asset/data_distribution.png)

Please check the paper for more details.

- **Curated by:** Jen-Hao(Andy) Cheng(andyhci@uw.edu), Yuan-Hao Ho (n28081527@gs.ncku.edu.tw) from [Information Processing Lab](https://ipl-uw.github.io/) at University of Washington
- **License:** [CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)
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### Dataset Sources

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- **Repository including data processing and baseline method codes:** [RT-POSE](https://github.com/ipl-uw/RT-POSE)
- **Paper:** To be viewed on arxiv.

## Uses

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1. Download the dataset from Hugging Face (Total data size: ~1.2 TB)
2. Follow the [data processing tool](https://github.com/ipl-uw/RT-POSE/data_processing) to process radar ADC samples into radar tensors. (Total data size of the downloaded data and saved radar tensors: ~41 TB)
3. Check the data loading and baseline training, testing codes in the same repo [RT-POSE](https://github.com/ipl-uw/RT-POSE)

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

To appear on arxiv

## Dataset Card Contact

Jen-Hao (Andy) Cheng, andyhci@uw.edu