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. Dataset Details Dataset Description Sensors The data collection hardware system comprises two RGB cameras, a non repetitive horizontal scanning LiDAR, and a cascade imaging radar module. Data Statics We collect the dataset in 40 scenes with indoor and outdoor environments. 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. 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 Repository including data processing and baseline method codes: RT POSE Paper:…
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