andaba/RT-Pose

Paper

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 radar ADC data, 4D radar tensors, stereo RGB images, and LiDAR point clouds. This integration marks a significant advancement in studying human pose analysis through multi-modality datasets.

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Dataset Details

Dataset Description

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Sensors

The data collection hardware system comprises two RGB cameras, a non-repetitive horizontal scanning LiDAR, and a cascade imaging radar module. [image]

Data Statics

We collect the dataset in 40 scenes with indoor and outdoor environments. [image]

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.

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Please check the paper for more details.

• —Curated by: Yuan-Hao Ho (n28081527@gs.ncku.edu.tw), Jen-Hao(Andy) Cheng(andyhci@uw.edu) from Information Processing Lab at University of Washington
• —License: CC BY-NC-SA

Dataset Sources

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• —Repository including data processing and baseline method codes: RT-POSE
• —Paper: Paper

Uses

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1. 1.Download the dataset from Hugging Face (Total data size: ~1.2 TB)
2. 2.Follow the data processing tool to process radar ADC samples into radar tensors. (Total data size of the downloaded data and saved radar tensors: ~41 TB)
3. 3.Check the data loading and baseline method's training and testing codes in the same repo RT-POSE

Citation

BibTeX:

@article{rtpose2024, title={RT-Pose: A 4D Radar Tensor-based 3D Human Pose Estimation and Localization Benchmark}, author={Yuan-Hao Ho and Jen-Hao Cheng and Sheng Yao Kuan and Zhongyu Jiang and Wenhao Chai and Hsiang-Wei Huang and Chih-Lung Lin and Jenq-Neng Hwang}, journal={arXiv preprint arXiv:2407.13930}, year={2024} }