| --- |
| license: cc-by-4.0 |
| task_categories: |
| - depth-estimation |
| - robotics |
| language: |
| - en |
| tags: |
| - mmWave-radar |
| - FMCW |
| - imaging |
| - depth-image |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # TextRadar Dataset |
|
|
| TextRadar is a multimodal indoor RF sensing dataset introduced in the our paper **“3D Occupancy-Language Fields from mmWave Radar.”** |
|
|
| It contains synchronized high-resolution mmWave radar (TI MMWCAS-RF-EVM), LiDAR, and IMU measurements collected using a mobile robotic platform across **7 indoor buildings**. |
|
|
| The dataset supports research in RF imaging, depth estimation, robot localization, and multimodal perception. |
|
|
| ## Dataset Details |
|
|
| - **~20k synchronized RF/LiDAR samples** |
| - **7 indoor buildings** |
| - **25 robot trajectories** |
| - Approximately **461 GB of processed data** |
| - **Curated by:** Haowen Lai, Zhiwei Zheng, and Mingmin Zhao, WAVES Lab, University of Pennsylvania |
| - **License:** CC BY 4.0 |
|
|
| ### Links |
|
|
| - **Paper:** *3D Occupancy-Language Fields from mmWave Radar*, arXiv 2026 |
|
|
| ## Dataset Structure |
|
|
| The dataset contains two main components. |
|
|
| ### Raw Data |
|
|
| Raw recordings include: |
|
|
| - mmWave radar I/Q measurements |
| - synchronized LiDAR measurements |
| - synchronized IMU measurements |
| - timestamps |
|
|
| The raw data are intended for researchers working on radar signal processing, motion estimation, beamforming, and RF imaging. |
|
|
| ### Processed Data |
|
|
| Processed data include: |
|
|
| - 3D RF heatmaps |
| - LiDAR-derived range ground truth |
| - glass masks |
|
|
| These data can be used directly for training and evaluating machine-learning models without rerunning the full radar-processing pipeline. |
|
|
| ## Recommended Split |
|
|
| The original TextRadar experiments use **leave-one-building-out (LOBO)** evaluation. |
|
|
| For each experiment: |
|
|
| - 6 buildings are used for training |
| - 1 building is held out for testing |
|
|
| This split is recommended when evaluating generalization to unseen environments. |
|
|
| ## Data Collection |
|
|
| Data were collected using a robotic platform equipped with: |
|
|
| - **TI MMWCAS-RF-EVM** mmWave FMCW radar |
| - **Ouster 64-beam LiDAR** |
| - IMU |
| - rotating radar assembly |
|
|
| The rotating radar forms a synthetic cylindrical array for panoramic 3D RF imaging. |
|
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| LiDAR measurements provide geometric ground truth, while synchronized radar measurements are processed into 3D RF heatmaps. |
|
|
| ## Annotations |
|
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| Range and geometric ground truth are derived primarily from synchronized LiDAR measurements. |
|
|
| ## Uses |
|
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| TextRadar is intended for research in areas including: |
|
|
| - RF and mmWave sensing |
| - 3D RF imaging |
| - depth and range estimation |
| - surface-normal estimation |
| - robot localization |
| - RF–LiDAR multimodal learning |
| - cross-building generalization |
|
|
| ## Limitations |
|
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| The dataset was collected in **7 indoor buildings** and may not generalize directly to outdoor environments. |
|
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| Results may also depend on the specific radar configuration, sensor mounting, and acquisition setup used in TextRadar. |
|
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| LiDAR is used as geometric ground truth but can be unreliable on transparent surfaces such as glass. Glass masks are included to help identify these regions. |
|
|
| ## Citation |
|
|
| If you use TextRadar, please cite: |
|
|
| ```bibtex |
| @article{TextRadar, |
| title={3D Occupancy-Language Fields from mmWave Radar}, |
| author={Lai, Haowen and Zheng, Zhiwei and Zhao, Mingmin}, |
| journal={arXiv preprint arXiv:2610.37929}, |
| year={2026} |
| } |
| |
| ``` |
