Origametry/origami-direct-tiny

Origami Direct Crease Pattern Dataset

A multiview image dataset for training models to predict complete origami crease patterns from 3D visualizations.

Task

Given 14 camera views of a folded origami shape, predict the complete crease pattern as a FOLD JSON (vertices, edges, mountain/valley assignments).

Dataset Structure

Each example contains:

Camera Views (14 per example)

• —6 face views: face_pos_x, face_neg_x, face_pos_y, face_neg_y, face_pos_z, face_neg_z
• —8 corner views: corner_ppp, corner_ppn, corner_pnp, corner_pnn, corner_npp, corner_npn, corner_nnp, corner_nnn

Splits

Pattern Strategies

Usage

from datasets import load_dataset
from PIL import Image

ds = load_dataset("YOUR_USERNAME/origami-direct")

example = ds["train"][0]
print(example["id"])              # "grid4_4c_0000"
print(len(example["images"]))     # 14
print(example["num_creases"])     # 4

# Access the complete crease pattern
fold = example["fold"]
print(fold["edges_assignment"])   # ["B", "B", ..., "M", "V", ...]

# Load a view
img = Image.open(example["images"][6])  # corner_ppp

FOLD Format

The fold field uses the FOLD format (JSON-based):

{
  "vertices_coords": [[0, 0], [0.5, 0], ...],
  "edges_vertices": [[0, 1], [1, 2], ...],
  "edges_assignment": ["B", "M", "V", ...],
  "edges_foldAngle": [0, -180, 180, ...],
  "faces_vertices": [[0, 1, 2], ...]
}

Edge assignments: B = boundary, M = mountain, V = valley, F = flat (structural).

Generation

Generated using OrigamiAnnotator with rendering via OrigamiSimulator.

• —Crease patterns built via tree search with Kawasaki/Maekawa theorem verification
• —3D renderings produced by OrigamiSimulator (GPU physics simulation) at 60% fold
• —Post-simulation intersection checking for quality filtering

Related

• —origami-step-by-step — step-by-step version of this dataset (predict one crease at a time)