| --- |
| 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} |
| } |
| ``` |
|
|
