Datasetpublic

bayang/shape

Shape Geometry Dataset Synthetic graph-based centerline representations of 3D geometric motifs (pipe-like structures). JSON Schema dataset.json is an array of shape records. Each record: { "category": "arc_90", "nodes": [[x, y, z], ...], "edges": [[i, j], ...], "features": { "curvature": [0.0, 0.1, ...], "segment_angle": [0.0, 160.5, ...] } } Field Type Description category string Shape class label (e.g. straight, arc_90, corner)… See the full description on the dataset page: https://huggingface.co/datasets/bayang/shape.

sourceHugging Facemitupdated 8mo agoView on Hugging Face

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Dataset Card

Shape Geometry Dataset

Synthetic graph-based centerline representations of 3D geometric motifs (pipe-like structures).

JSON Schema

dataset.json is an array of shape records. Each record:

json

{
  "category": "arc_90",
  "nodes": [[x, y, z], ...],
  "edges": [[i, j], ...],
  "features": {
    "curvature": [0.0, 0.1, ...],
    "segment_angle": [0.0, 160.5, ...]
  }
}

Field	Type	Description
`category`	string	Shape class label (e.g. `straight`, `arc_90`, `corner`)
`nodes`	float[][] (N×3)	3D points with ~1-unit spacing between neighbors
`edges`	int[][] (E×2)	Index pairs connecting nodes
`features.curvature`	float[] (N)	Menger curvature per node (0 at endpoints/hubs)
`features.segment_angle`	float[] (N)	Angle in degrees between edges at each node

Categories

Category	What it is	Samples
`straight`	Random-length line segment (5–20 units)	[image]
`arc_10`	10° gentle arc, R=10	[image]
`arc_20`	20° arc	[image]
`arc_30`	30° arc	[image]
`arc_40`	40° arc	[image]
`arc_50`	50° arc	[image]
`arc_60`	60° arc	[image]
`arc_70`	70° arc	[image]
`arc_80`	80° arc	[image]
`arc_90`	90° quarter-circle bend	[image]
`arc_100`	100° arc	[image]
`arc_110`	110° arc	[image]
`arc_120`	120° wide arc	[image]
`arc_130`	130° arc	[image]
`arc_140`	140° arc	[image]
`arc_150`	150° near-semicircle	[image]
`arc_160`	160° arc	[image]
`arc_170`	170° near-full semicircle	[image]
`corner`	Two straight arms at a sharp 90° angle	[image]
`junction_T`	Through-pipe with perpendicular branch (T-shape)	[image]
`junction_Y`	Trunk splitting into two splayed arms (Y-shape)	[image]

Why this structure?

—Nodes + edges = graph — directly loadable into GNN frameworks (PyTorch Geometric, DGL).
—Unit spacing — standardises graph density across shapes; a 15-unit straight and a 90° arc at R=10 both have proportional node counts.
—Random rigid pose — every sample gets a random 3D rotation + translation so the model can't memorise orientation.
—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.
—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.

Regenerate

bash

python generate_geometry_dataset.py

Generates 10000 samples per category (17 arc classes + straight + corner + junctionT + junctionY = 21 categories, 21k samples total).