| --- |
| license: mit |
| task_categories: |
| - image-to-text |
| - visual-question-answering |
| tags: |
| - origami |
| - crease-pattern |
| - fold |
| - multiview |
| - 3d-to-code |
| size_categories: |
| - n<1K |
| --- |
| |
| # 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: |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `id` | string | Unique sample ID (e.g., `grid4_4c_0000`) | |
| | `images` | list[string] | 14 PNG paths — 6 face views + 8 corner views | |
| | `fold` | dict | Complete crease pattern in FOLD format | |
| | `difficulty` | string | `"easy"`, `"medium"`, or `"hard"` | |
| | `num_creases` | int | Number of mountain/valley creases | |
|
|
| ### 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 | 12 | |
| | val | 1 | |
| | test | 2 | |
|
|
| ## Pattern Strategies |
|
|
| | 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-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](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 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](https://huggingface.co/datasets/YOUR_USERNAME/origami-step-by-step) — step-by-step version of this dataset (predict one crease at a time) |
|
|
