| --- |
| pretty_name: GRADE Raw Multimodal Dataset |
| viewer: false |
| --- |
| |
| # GRADE dataset |
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| This dataset release contains raw recordings for **GRADE: Single-Frame Generative Radar Depth Estimation Under Visual Degradation**, accepted to **ACM MobiCom 2026**. See the [paper](https://arxiv.org/abs/2609.10756) and [project website](https://phi-lab-rice.github.io/GRADE/). |
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| The recordings were made with a TI IWR1843BOOST radar (with DCA1000EVM), a ZED 2i stereo camera, and a DJI Action 5 Pro camera. Evaluation sequences also used a MAX30105 smoke sensor. **All 70 public sequences now include anonymized ZED and DJI RGB videos.** The original ZED SVO2 and DJI recordings are not published. |
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| ## Folders and splits |
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| ```text |
| GRADE_Train_Raw/ 50 sequence folders |
| GRADE_Eval_Raw/ 20 smoke evaluation sequence folders |
| processing_code/ processor.py, requirements.txt, and utils/ |
| ``` |
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| **Split rule:** Within `GRADE_Train_Raw`, the 10 folders with `Dell` or `Keck` in their names are for **validation**, not model training. This includes the `Smoke-Dell-*` and `Smoke-keck-*` folders. The other 40 folders are for **training**. All 20 folders in `GRADE_Eval_Raw` are for **evaluation**. Do not treat every folder under `GRADE_Train_Raw` as training data. |
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| Each sequence folder contains these timestamp and radar files: |
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| ```text |
| radar_*.h5 raw radar samples and timestamps |
| camera_timestamps_*.h5 ZED timestamps |
| dji_timestamps_*.h5 DJI timestamps |
| max30105_*.h5 MAX30105 samples and timestamps (evaluation only) |
| ``` |
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| Training sequences have no MAX30105 file. The processor handles that automatically. |
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| Each sequence folder on the Hub also has: |
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| ```text |
| zed_video_anonymized.mkv calibrated rectified-left ZED RGB, defaced, 1280x720 |
| dji_video_anonymized.mkv fisheye-corrected and cropped DJI RGB, defaced, 1280x720 |
| ``` |
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| Both videos use **lossy H.265/HEVC Main profile at CRF 14**, with 8-bit YUV 4:2:0 pixels in Matroska. Faces were blurred before this encoding. The original anonymized lossless RGB files were used as the transcoding inputs. Three original DJI recordings (`brk-2`, `brk-3`, and `brk-basement`) have 8, 3, and 6 untimed terminal video frames, respectively. Their anonymized videos omit those frames during CRF encoding, so every released DJI frame has a matching entry in its original DJI timestamp H5. No timestamps were removed from the H5 files. |
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| The SDK-free processor also requires `zed_depth.h5`: full-frame ZED depth aligned with the ZED RGB video at 1280×720. **This depth file has not yet been uploaded to the Hub.** The local prepared file has a `depth_mm` dataset of shape `(frames, 720, 1280)`, dtype `uint16`, with no HDF5 compression, matching the radar H5 compression setting. Zero marks invalid depth; positive values are millimeters rounded to 1 mm. Depth was computed from the original SVO2 before RGB face blurring. The ZED camera clock is `camera_timestamps_*.h5`; there is no separate SVO2 timestamp file. Frame index `i` in that H5 corresponds to frame `i` in the ZED video and depth H5, up to the exported frame count. The processor trims surplus terminal timestamps to that count. |
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| The local preparation folders retain the original DJI recordings and ZED SVO2 files for preservation. **Do not include those originals in a public upload.** |
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| ## Process a sequence |
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| `processing_code/processor.py` first matches the H5 sensor timestamps, then extracts synchronized radar, ZED RGB/depth, and DJI RGB frames. It saves one output folder per sequence. `processing_code/utils/` contains the sensor extractors, radar processing, synchronization, and point-cloud code. The processor requires both anonymized videos and the matching `zed_depth.h5`; because the depth H5 is not on the Hub yet, the current public download alone cannot run the full camera pipeline. |
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| 1. From this repository's root folder, create a Python environment and install the dependencies: |
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| ```bash |
| python3.12 -m venv .venv |
| source .venv/bin/activate |
| python -m pip install -r processing_code/requirements.txt |
| ``` |
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| The public processor does not require the ZED SDK or an NVIDIA GPU. It uses OpenCV to decode the HEVC videos and h5py to read precomputed metric depth. |
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| 2. Process one evaluation sequence after its prepared camera files are available: |
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| ```bash |
| python processing_code/processor.py \ |
| --dataset GRADE_Eval_Raw \ |
| --sequences Smoke-keck-2-3 \ |
| --output-dir processed_eval |
| ``` |
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| For a training sequence, use `--dataset GRADE_Train_Raw --sequences AlicePratt-1`. Leave out `--sequences` to process every folder in the chosen dataset. |
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| The default output for one evaluation sequence looks like this: |
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| ```text |
| processed_eval/Smoke-keck-2-3/ |
| ├── radar.npy complex64 radar spectrum, (N, 64, 2, 8, 256) |
| ├── dji_rgb.npy uint8 RGB, (N, 504, 896, 3) |
| ├── zed_rgb.npy uint8 RGB, (N, 504, 896, 3) |
| ├── zed_depth.npy uint16 depth in millimeters, (N, 720, 1280) |
| ├── max30105.npy uint32, 3 channels; only when the sensor file exists |
| ├── sync_triples.csv original radar, ZED, and DJI frame indices |
| ├── metadata.json shapes, timestamps, and processing settings |
| └── pcd/ |
| ├── pcd_0.npy float32 XYZ points, (number_of_points, 3) |
| └── ... |
| ``` |
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| Each point cloud is a separate `.npy` file because its number of points varies by frame. `N` is the number of synchronized frames kept for that sequence. |
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| ### Useful options |
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| | Option | What it does | |
| | --- | --- | |
| | `--dataset PATH` | Choose `GRADE_Train_Raw` or `GRADE_Eval_Raw` (required). | |
| | `--sequences NAME [NAME ...]` | Process selected sequences; omit to process all. | |
| | `--output-dir PATH` | Choose where outputs go (default: `processed`). | |
| | `--no-radar`, `--no-camera`, `--no-dji` | Skip the named processed output. `--no-radar` skips the spectrum; use `--no-pcd` to skip point clouds. All three sensor timestamps are still used for synchronization. | |
| | `--no-pcd` | Skip point clouds (they are produced by default). | |
| | `--no-doppler` | Save `radar_no_doppler.npy` instead of the full `radar.npy`. | |
| | `--list-unprocessed` | List sequences without an output `metadata.json`. | |
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| `--rgb-codec` is a legacy argument and has no effect on the current `.npy` output. Run `python processing_code/processor.py --help` for the complete CLI help. |
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| ## Please cite GRADE if you find this dataset useful in your research |
|
|
| ```bibtex |
| @inproceedings{zhao2026grade, |
| author = {Bin Zhao and Patrick Chiou and Nakul Garg}, |
| title = {{GRADE}: Single-Frame Generative Radar Depth Estimation Under Visual Degradation}, |
| booktitle = {Proceedings of the 32nd Annual International Conference on Mobile Computing and Networking}, |
| series = {MobiCom '26}, |
| year = {2026}, |
| publisher = {ACM}, |
| address = {New York, NY, USA}, |
| numpages = {15}, |
| doi = {10.1145/3795866.3844478}, |
| url = {https://doi.org/10.1145/3795866.3844478} |
| } |
| ``` |
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