---
pretty_name: RISE-data-4scenes
tags:
- radar
- mmwave
- indoor-scene-understanding
- layout-reconstruction
- object-detection
task_categories:
- object-detection
size_categories:
- 1K<n<10K
---

# RISE-data-4scenes

Radar frames for four real indoor scenes of the RISE benchmark
(*RISE: Single Static Radar-based Indoor Scene Understanding*, CVPR 2026, arXiv 2511.14019),
packed in the python-native `bame` profile that the released code reads directly.
No MATLAB is needed.

Code: https://github.com/kaichen-z/RISE · Project page: https://rise-cvpr.github.io/

| case | scene | trajectories | background | frames (traj / bkg shipped) |
|------|-------|--------------|------------|-----------------------------|
| 19 | `Raw_Data_3/scene3_set2` | `_r1 _r2 _r3` | `scene3_set2_bkg` | 1,108 / 13 |
| 26 | `Raw_Data_5/scene5_set1` | `_r1 _r2 _r3` | `scene5_set1_bkg` | 1,003 / 13 |
| 42 | `Raw_Data_9/scene9_set1` | `_r1 _r2 _r3` | `scene9_set1_bkg` | 1,127 / 13 |
| 50 | `Raw_Data_11/scene11_set1` | `_r1 _r2 _r3` | `scene11_set1_bkg` | 1,103 / 13 |

Every scene has furniture; the evaluation ground truth for all four cases ships with the
code (`5_diffusion/data/GT/{obs,wall}/<case>_gt.png`).

## Download

```bash
hf download kaichen-z/RISE RISE-data-4scenes.zip --repo-type dataset --local-dir .
unzip RISE-data-4scenes.zip
sha256sum -c RISE-data-4scenes.zip.sha256     # optional
```

## Layout

```
RISE-data-4scenes/
  Raw_Data_3/
    scene3_set2_r1/preprocess/
      original_mat_1_<n>.npy      complex64, shape (256 range, 1 doppler, 16 RX, 12 TX), 393,216 B
      original_hyp_1_<n>.json     range-FFT parameters for that frame (7.3 KB)
      python_frames.json          {"profile": "bame", "every": 1, ...}
    scene3_set2_r2/  scene3_set2_r3/            same
    scene3_set2_bkg/preprocess/                 empty-room reference, every 30th frame only ("every": 30)
    *.ply                                       lidar scans of the room (reference only, not read by the code)
    Cascade_Capture_22xx.{mmwave,setup}.json    radar configuration (provenance only)
  Raw_Data_5/  Raw_Data_9/  Raw_Data_11/        same structure
```

Point the code at the unzipped folder and run a case:

```bash
export RISE_DATA_ROOT=/path/to/RISE-data-4scenes
export RISE_DEVICE=cuda            # cuda | mps | cpu (3D CFAR needs a GPU)
cd 3_bame      && python run.py --case 42
cd ../4_inversion && python run.py --case 42 --step associate && python run.py --case 42 --step layout
```

## Format notes

- `bame` profile = doppler bin 0 of the post-MATLAB cube, stored as complex64. This is exact
  for the released BAME pipeline (`3_bame/bame.py` slices `matrix[:, :1]` before a per-column
  range FFT); it is not sufficient for the MVDR baseline, which needs all 16 doppler bins.
- Frame index `<n>` is the capture frame number; trajectory captures start at 0, background
  captures at whatever frame the recording started on. The code sorts by `<n>`.
- Packed with `tools/pack_bame.pl` (stdlib Perl), verified byte-identical to
  `tools/pack_frames.py --profile bame` on the same frame.
- Raw `.bin` captures and the MATLAB `.mat` cache are not included.

## Citation

```bibtex
@inproceedings{zhou2026rise,
  title={Rise: single static radar-based indoor scene understanding},
  author={Zhou, Kaichen and Dodds, Laura and Afzal, Sayed Saad and Adib, Fadel},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={32194--32205},
  year={2026}
}
```