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README.md

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+---
+license: mit
+task_categories:
+  - image-to-text
+  - visual-question-answering
+tags:
+  - origami
+  - crease-pattern
+  - fold
+  - multiview
+  - step-by-step
+  - 3d-to-code
+size_categories:
+  - n<1K
+---
+
+# Origami Step-by-Step Crease Pattern Dataset
+
+A multiview image dataset for training models to infer origami crease patterns from 3D visualizations, one fold at a time.
+
+## Task
+
+Given **14 camera views** of a partially-folded origami sheet, predict the **next crease line** to add (edge position + mountain/valley assignment).
+
+This mirrors a step-by-step folding process: starting from a blank sheet, each step adds one crease and the model must predict the next one from the current 3D visualization.
+
+## Dataset Structure
+
+Each example contains:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | string | Unique example ID (e.g., `grid3_3c_0000_step_001`) |
+| `images` | list[string] | 14 PNG paths — 6 face views + 8 corner views |
+| `partial_fold` | dict | Current crease pattern in FOLD format (vertices, edges, assignments) |
+| `next_crease` | dict | The crease to predict: `{"edge": [v0, v1], "assignment": "M" or "V"}` |
+| `step` | int | Current step index (0-based) |
+| `steps_remaining` | int | Steps left to complete the pattern |
+| `difficulty` | string | `"easy"`, `"medium"`, or `"hard"` |
+
+### 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
+
+| Split | Examples |
+|-------|----------|
+| train | 75 |
+| val | 9 |
+| test | 10 |
+
+## Pattern Strategies
+
+Patterns are generated using four strategies for diversity:
+
+| Strategy | Description | Interior vertices |
+|----------|-------------|-------------------|
+| `grid` | Creases on NxN grid | Grid intersections |
+| `singlevertex` | Radial creases from center | 1 (center) |
+| `multivertex` | Random interior connections | N random points |
+| `parallel` | Parallel lines at an angle | None |
+
+## Usage
+
+```python
+from datasets import load_dataset
+from PIL import Image
+
+ds = load_dataset("YOUR_USERNAME/origami-step-by-step")
+
+example = ds["train"][0]
+print(example["id"])                # "grid3_3c_0000_step_001"
+print(len(example["images"]))       # 14
+print(example["next_crease"])       # {"edge": [3, 7], "assignment": "M"}
+print(example["steps_remaining"])   # 2
+
+# Load one view
+img = Image.open(example["images"][6])  # corner_ppp
+```
+
+### FOLD Format
+
+The `partial_fold` field uses the [FOLD format](https://github.com/edemaine/fold) (JSON-based):
+
+```json
+{
+  "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](https://github.com/YOUR_USERNAME/OrigamiAnnotator) with rendering via [OrigamiSimulator](https://origamisimulator.org/).
+
+- Crease patterns built incrementally 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