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--- |
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license: mit |
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task_categories: |
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- graph-ml |
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tags: |
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- synthetic |
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- geometry |
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- graph-neural-network |
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- 3d |
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- centerline |
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- pipe-structures |
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pretty_name: Shape Geometry Dataset |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Shape Geometry Dataset |
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Synthetic graph-based centerline representations of 3D geometric motifs (pipe-like structures). |
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## JSON Schema |
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`dataset.json` is an array of shape records. Each record: |
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```json |
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{ |
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"category": "arc_90", |
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"nodes": [[x, y, z], ...], |
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"edges": [[i, j], ...], |
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"features": { |
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"curvature": [0.0, 0.1, ...], |
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"segment_angle": [0.0, 160.5, ...] |
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} |
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} |
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``` |
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| Field | Type | Description | |
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|-------|------|-------------| |
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| `category` | string | Shape class label (e.g. `straight`, `arc_90`, `corner`) | |
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| `nodes` | float[][] (N×3) | 3D points with ~1-unit spacing between neighbors | |
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| `edges` | int[][] (E×2) | Index pairs connecting nodes | |
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| `features.curvature` | float[] (N) | Menger curvature per node (0 at endpoints/hubs) | |
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| `features.segment_angle` | float[] (N) | Angle in degrees between edges at each node | |
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## Categories |
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| Category | What it is | Samples | |
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|----------|-----------|---------| |
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| `straight` | Random-length line segment (5–20 units) |  | |
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| `arc_10` | 10° gentle arc, R=10 |  | |
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| `arc_20` | 20° arc |  | |
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| `arc_30` | 30° arc |  | |
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| `arc_40` | 40° arc |  | |
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| `arc_50` | 50° arc |  | |
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| `arc_60` | 60° arc |  | |
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| `arc_70` | 70° arc |  | |
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| `arc_80` | 80° arc |  | |
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| `arc_90` | 90° quarter-circle bend |  | |
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| `arc_100` | 100° arc |  | |
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| `arc_110` | 110° arc |  | |
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| `arc_120` | 120° wide arc |  | |
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| `arc_130` | 130° arc |  | |
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| `arc_140` | 140° arc |  | |
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| `arc_150` | 150° near-semicircle |  | |
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| `arc_160` | 160° arc |  | |
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| `arc_170` | 170° near-full semicircle |  | |
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| `corner` | Two straight arms at a sharp 90° angle |  | |
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| `junction_T` | Through-pipe with perpendicular branch (T-shape) |  | |
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| `junction_Y` | Trunk splitting into two splayed arms (Y-shape) |  | |
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## Why this structure? |
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- **Nodes + edges = graph** — directly loadable into GNN frameworks (PyTorch Geometric, DGL). |
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- **Unit spacing** — standardises graph density across shapes; a 15-unit straight and a 90° arc at R=10 both have proportional node counts. |
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- **Random rigid pose** — every sample gets a random 3D rotation + translation so the model can't memorise orientation. |
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- **Per-node features** — curvature and segment angle give the network local geometric cues beyond raw xyz, helping distinguish arcs from straights even when node counts overlap. |
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- **Junctions share a hub node** — T and Y shapes have one node connected to 3 edges, matching real pipe topology and giving the GNN a clear topological signal. |
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## Regenerate |
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```bash |
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python generate_geometry_dataset.py |
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``` |
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Generates 10000 samples per category (17 arc classes + straight + corner + junction_T + junction_Y = 21 categories, 21k samples total). |
