--- pretty_name: GRADE Raw Multimodal Dataset viewer: false --- # GRADE dataset 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/). 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. ## Folders and splits ```text GRADE_Train_Raw/ 50 sequence folders GRADE_Eval_Raw/ 20 smoke evaluation sequence folders processing_code/ processor.py, requirements.txt, and utils/ ``` **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. Each sequence folder contains these timestamp and radar files: ```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) ``` Training sequences have no MAX30105 file. The processor handles that automatically. Each sequence folder on the Hub also has: ```text zed_video_anonymized.mkv calibrated rectified-left ZED RGB, defaced, 1280x720 dji_video_anonymized.mkv fisheye-corrected and cropped DJI RGB, defaced, 1280x720 ``` 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. 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. The local preparation folders retain the original DJI recordings and ZED SVO2 files for preservation. **Do not include those originals in a public upload.** ## Process a sequence `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. 1. From this repository's root folder, create a Python environment and install the dependencies: ```bash python3.12 -m venv .venv source .venv/bin/activate python -m pip install -r processing_code/requirements.txt ``` 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. 2. Process one evaluation sequence after its prepared camera files are available: ```bash python processing_code/processor.py \ --dataset GRADE_Eval_Raw \ --sequences Smoke-keck-2-3 \ --output-dir processed_eval ``` For a training sequence, use `--dataset GRADE_Train_Raw --sequences AlicePratt-1`. Leave out `--sequences` to process every folder in the chosen dataset. The default output for one evaluation sequence looks like this: ```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) └── ... ``` 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. ### Useful options | 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`. | `--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. ## 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} } ```