Blame for README.md

---
license: cc-by-4.0
task_categories:
- depth-estimation
- object-detection
- robotics
language:
- en
tags:
- mmWave-radar
- FMCW
- imaging
- depth-image
- semantics
size_categories:
- 10K<n<100K
---

# PanoRadar Dataset

PanoRadar is a multimodal indoor RF sensing dataset introduced in the MobiCom 2024 paper **“Enabling Visual Recognition at Radio Frequency.”**

It contains synchronized mmWave radar (AWR1843), LiDAR, and IMU measurements collected using a mobile robotic platform across **12 indoor buildings**.

The dataset supports research in RF imaging, depth estimation, semantic segmentation, object detection, robot localization, and multimodal perception.

## Dataset Details

- **11,033 synchronized RF/LiDAR samples**
- **12 indoor buildings**
- **20,546 annotated object instances**
- Approximately **461 GB of processed data**
- **Curated by:** Haowen Lai, Gaoxiang Luo, Yifei Liu, and Mingmin Zhao, WAVES Lab, University of Pennsylvania
- **License:** CC BY 4.0

### Links

- **Repository:** https://github.com/penn-waves-lab/PanoRadar
- **Project:** https://penn-waves-lab.github.io/projects/panoradar
- **Paper:** *Enabling Visual Recognition at Radio Frequency*, ACM MobiCom 2024
- **DOI:** `10.1145/3636534.3649369`
- **Demo:** https://youtu.be/dKyQ1XuPorU

## 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
- moving and static robot trajectories

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
- surface-normal ground truth
- glass masks
- semantic segmentation labels
- object-detection labels

These data can be used directly for training and evaluating machine-learning models without rerunning the full radar-processing pipeline.

## Recommended Split

The original PanoRadar experiments use **leave-one-building-out (LOBO)** evaluation.

For each experiment:

- 11 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 AWR1843** 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.

Semantic segmentation and object-detection annotations were generated using a combination of machine-assisted labeling and human correction.

The dataset contains:

- **11,033 semantic and surface-normal annotations**
- **20,546 object instances**

## Uses

PanoRadar is intended for research in areas including:

- RF and mmWave sensing
- 3D RF imaging
- depth and range estimation
- surface-normal estimation
- semantic segmentation
- object detection
- robot localization
- RF–LiDAR multimodal learning
- cross-building generalization

## Limitations

The dataset was collected in **12 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 PanoRadar.

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.

Semantic and object annotations were produced using machine-assisted labeling with human correction and may contain annotation errors.

## Citation

If you use PanoRadar, please cite:

```bibtex
@inproceedings{PanoRadar,
  author    = {Lai, Haowen and Luo, Gaoxiang and Liu, Yifei and Zhao, Mingmin},
  title     = {Enabling Visual Recognition at Radio Frequency},
  booktitle = {Proceedings of the 30th Annual International Conference on Mobile Computing and Networking},
  pages     = {388--403},
  year      = {2024},
  doi       = {10.1145/3636534.3649369}
}
```

## Contact

For questions or issues, please use the PanoRadar GitHub repository:

https://github.com/penn-waves-lab/PanoRadar