| --- |
| license: cc-by-4.0 |
| task_categories: |
| - other |
| language: |
| - en |
| tags: |
| - 3d-building-matching |
| - geometric-entity-resolution |
| - cross-lod |
| - zero-shot |
| - few-shot |
| - cityjson |
| - plateau |
| - 3dbag |
| pretty_name: STER — 3D Building Geometric Entity Resolution Benchmark |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| # STER: Zero-shot 3D Geometric Entity Resolution Benchmark |
|
|
| > Multi-city, cross-LoD 3D building matching benchmark for the NS-D2S paper (AAAI 2026). |
| > Strictly follows the [3dSAGER (SIGMOD 2026)](https://github.com/BarGenossar/3dSAGER) methodology and data format. |
|
|
| ## Dataset Overview |
|
|
| | Dataset | City | Country | Buildings | LOD Source | Urban Typology | |
| |---------|------|---------|-----------|------------|----------------| |
| | amsterdam | Amsterdam | NL | 123,259 | 3DBAG LOD1.2/1.3/2.2 | Historic canal city | |
| | rotterdam | Rotterdam | NL | 152,694 | 3DBAG LOD1.2/1.3/2.2 | Post-war modern | |
| | hague | Den Haag | NL | 181,491 | 3DBAG LOD1.2/1.3/2.2 | Administrative center | |
| | utrecht | Utrecht | NL | 121,293 | 3DBAG LOD1.2/1.3/2.2 | Historic university city | |
| | eindhoven | Eindhoven | NL | 181,811 | 3DBAG LOD1.2/1.3/2.2 | Modern industrial | |
| | groningen | Groningen | NL | 72,696 | 3DBAG LOD1.2/1.3/2.2 | Northern university town | |
| | maastricht | Maastricht | NL | 73,147 | 3DBAG LOD1.2/1.3/2.2 | Historic small city | |
| | chiyoda | Chiyoda, Tokyo | JP | 8,438 | PLATEAU LOD1/LOD2 | Government/business core | |
| | chuo | Chuo, Tokyo | JP | 8,507 | PLATEAU LOD1/LOD2 | Historic commercial (Ginza) | |
| | shinjuku | Shinjuku, Tokyo | JP | 2,947 | PLATEAU LOD1/LOD2 | Skyscraper/entertainment | |
| | setagaya | Setagaya, Tokyo | JP | 8,415 | PLATEAU LOD1/LOD2 | Residential suburban | |
| | minato | Minato, Tokyo | JP | 9,023 | PLATEAU LOD1/LOD2 | Business/embassy district | |
| | bunkyo | Bunkyo, Tokyo | JP | 3,334 | PLATEAU LOD1/LOD2 | Academic/residential | |
| | koto | Koto, Tokyo | JP | 5,279 | PLATEAU LOD1/LOD2 | Waterfront/new development | |
| | ota | Ota, Tokyo | JP | 1,318 | PLATEAU LOD1/LOD2 | Mixed industrial/residential | |
| | kyoto | Kyoto | JP | 34,831 | PLATEAU LOD1/LOD2 | Historic ancient capital | |
| | osaka | Osaka | JP | 3,945 | PLATEAU LOD1/LOD2 | Metropolitan commercial | |
| | sakai | Sakai | JP | 4,128 | PLATEAU LOD1/LOD2 | Satellite industrial city | |
|
|
| **Total: 18 cities, 996,590 buildings** |
|
|
| ## Data Format |
|
|
| Each city directory (`data/{city}/`) contains exactly 5 files: |
|
|
| | File | Description | |
| |------|-------------| |
| | `object_dict_raw.joblib` | **Primary data**: SIGMOD-compatible 3D building meshes | |
| | `manifest.json` | Per-city metadata (bbox, filter params, timestamps) | |
| | `{city}_seed1.pkl` | Train/test partition (seed 1) | |
| | `{city}_seed2.pkl` | Train/test partition (seed 2) | |
| | `{city}_seed3.pkl` | Train/test partition (seed 3) | |
|
|
| ### object_dict_raw.joblib (SIGMOD Format) |
|
|
| ```python |
| { |
| 'cands': { # LOD1/LOD1.2 (coarse source) |
| building_id: { |
| 'polygon_mesh': [[[x,y,z],...], ...], |
| 'vertices': np.array([[x,y,z], ...]), # (N, 3) |
| 'centroid': np.array([x, y, z]), |
| }, ... |
| }, |
| 'index': { # LOD2/LOD2.2 (detailed source) |
| building_id: { ... }, ... |
| }, |
| 'mapping_dict': { # integer index ↔ building ID |
| 'cands': {0: id1, 1: id2, ...}, |
| 'index': {0: id1, 1: id2, ...}, |
| }, |
| 'inv_mapping_dict': { # building ID ↔ integer index |
| 'cands': {id1: 0, id2: 1, ...}, |
| 'index': {id1: 0, id2: 1, ...}, |
| }, |
| } |
| ``` |
|
|
| ### Partition Files ({city}_seed{1,2,3}.pkl) |
| |
| ```python |
| { |
| 'train': { |
| 'negative_sampling': { |
| 'small': {2: [(cand_idx, index_idx), ...], 5: [...]}, |
| 'large': {2: [...], 5: [...]} |
| } |
| }, |
| 'test': { |
| 'matching': { |
| 'negative_sampling': {'small': {...}, 'large': {...}}, |
| 'blocking-based': {'small': {2: [...], 5: [...]}, 'large': {...}} |
| }, |
| 'blocking': { |
| 'small': {'cands': {idx,...}, 'index': {idx,...}}, |
| 'large': {'cands': {idx,...}, 'index': {idx,...}} |
| } |
| } |
| } |
| ``` |
| |
| 80/20 spatial grid-based train/test split. 3 random seeds for statistical significance. |
|
|
| ### Quick Start |
|
|
| ```python |
| import joblib |
| |
| # Load dataset |
| od = joblib.load('data/amsterdam/object_dict_raw.joblib') |
| |
| # Compute 25 geometric properties via 3dSAGER pipeline |
| from object_properties import ObjectPropertiesProcessor |
| proc = ObjectPropertiesProcessor(od, vector_normalization=True) |
| # proc.prop_vals_dict is ready for PairProcessor → classifier training |
| |
| # Load partition |
| with open('data/amsterdam/amsterdam_seed1.pkl', 'rb') as f: |
| partition = pickle.load(f) |
| train_pairs = partition['train']['negative_sampling']['large'][2] |
| ``` |
|
|
| ## Cross-LoD Matching Paradigm |
|
|
| - **Dutch cities**: LOD1.2 (cands) vs LOD2.2 (index), shared BAG ID = GT match |
| - **Japan cities**: LOD1 (cands) vs LOD2 (index), shared PLATEAU building ID = GT match |
|
|
| Same building ID across LODs = positive match. No human annotation needed. |
|
|
| ## 25 Geometric Properties |
|
|
| `bounding_box_width`, `bounding_box_length`, `area`, `perimeter`, `perimeter_ind`, `volume`, `convex_hull_area`, `convex_hull_volume`, `ave_centroid_distance`, `height_diff`, `num_floors`, `axes_symmetry`, `compactness_2d`, `compactness_3d`, `density`, `elongation`, `shape_ind`, `hemisphericality`, `fractality`, `cubeness`, `circumference`, `aligned_bounding_box_width/length/height`, `num_vertices` |
|
|
| Log-normalized via official `ObjectPropertiesProcessor`. |
|
|
| ## Filtering |
|
|
| - Buildings with <10 polygon faces excluded |
| - Dutch: combined LOD1.2+LOD1.3+LOD2.2 ≥10 faces |
| - Japan: LOD1 ≥10 and LOD2 ≥10 faces |
|
|
| ## Coordinate Systems |
|
|
| - Dutch: EPSG:28992 (Amersfoort / RD New) |
| - Japan: JGD2011 (EPSG:6697) |
|
|
| ## Code |
|
|
| | Script | Description | |
| |--------|-------------| |
| | `code/object_properties.py` | 25 geometric property computation (3dSAGER official) | |
| | `code/build_benchmark.py` | 3DBAG Dutch multi-city builder | |
| | `code/build_plateau.py` | PLATEAU Japan city builder | |
| | `code/fix_sigmod_v2.py` | Format unification (SIGMOD object_dict) | |
| | `code/generate_partitions.py` | Train/test partition generator | |
| | `code/pipelines.py` | 3dSAGER pipeline (matching/blocking) | |
| | `code/main.py` | 3dSAGER entry point | |
| | `code/ster_*.py` | NS-D2S experiment scripts | |
|
|
| ## Citation |
|
|
| If you use this benchmark, please cite both this dataset and the original 3dSAGER paper. |
|
|
