Update README.md

README.md

@@ -1,3 +1,123 @@
 ---
 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.
+
+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:
+
+```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}
+}
+
+```