Datasets:

pennwaveslab
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TextRadar-Dataset

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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

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.

LiDAR measurements provide geometric ground truth, while synchronized radar measurements are processed into 3D RF heatmaps.

Annotations

Range and geometric ground truth are derived primarily from synchronized LiDAR measurements.

Uses

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

The dataset was collected in 7 indoor buildings and may not generalize directly to outdoor environments.

Results may also depend on the specific radar configuration, sensor mounting, and acquisition setup used in TextRadar.

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:

@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}
}

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Paper for pennwaveslab/TextRadar-Dataset

3D Occupancy-Language Fields from mmWave Radar

Paper • 2610.37929 • Published 18 days ago