---
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.