---
license: cc-by-nc-4.0
pretty_name: Intent2CAD
language:
- en
tags:
- cad
- cadquery
- text-to-cad
- parametric-modeling
configs:
- config_name: generation
  default: true
  data_files:
  - split: train
    path: generation/train.parquet
  - split: validation
    path: generation/validation.parquet
  - split: test
    path: generation/test.parquet
- config_name: editing
  data_files:
  - split: test
    path: editing/test.parquet
---

# Intent2CAD

Official dataset for **Intent2CAD: How Semantic–Parametric Supervision Shapes Text-to-CAD Generation**, accepted at **NeurIPS 2026**.

[Paper](https://arxiv.org/abs/2611.03449) · [Code](https://github.com/JinboYang94/Intent2CAD)

Intent2CAD provides natural language descriptions paired with executable CadQuery programs, along with source–target pairs for intent-preserving CAD editing. Each model has three aligned code representations:

- **Primitive:** low-level sketch and extrusion operations.
- **Semantic:** feature-level operations such as rectangles, polygons, slots, fillets, and arrays.
- **Semantic–parametric:** semantic features with shared parameters and derived expressions.

## Datasets

| Subset | Splits | Description |
|---|---|---|
| `generation` | train / validation / test | IntentCAD-100K: text descriptions and three aligned code representations. |
| `editing` | test | IntentCAD-Edit-1K: editing instructions with source and target programs for each representation. |

The editing dataset covers Feature Edit (FE), Consistency Edit (CE), and Derived-Relation Edit (DE), stored as `feature`, `consistency`, and `dependency`.

The editing examples are drawn from the generation test set.

## Data Format

**Generation**

| Field | Description |
|---|---|
| `id` | Sample identifier |
| `text` | Natural language description |
| `code_primitive` | Primitive CadQuery program |
| `code_semantic` | Semantic CadQuery program |
| `code_semparam` | Semantic–parametric CadQuery program |

**Editing**

Each example contains `id`, `category`, `instruction`, and a source–target pair for each representation: `source_primitive` / `target_primitive`, `source_semantic` / `target_semantic`, and `source_semparam` / `target_semparam`.

## Usage

```python
from datasets import load_dataset

generation = load_dataset("scottyjb/Intent2CAD", "generation", token=True)
train = generation["train"]
validation = generation["validation"]
test = generation["test"]

editing = load_dataset(
    "scottyjb/Intent2CAD", "editing", split="test", token=True
)
```

## Evaluation

Evaluation annotations are provided in `metadata/editing.tsv`, matched to examples by `id`.

They specify the loops checked for edit success, edit types, and relation subtypes.

Models should receive the source program and editing instruction. Target programs and evaluation annotations are used only for scoring.

## Citation

```bibtex
@inproceedings{yang2026intent2cad,
  title     = {{Intent2CAD}: How Semantic--Parametric Supervision Shapes Text-to-CAD Generation},
  author    = {Jinbo Yang and Mingyue Yuan and Shikai Jing},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2026}
}
```

## Acknowledgements

The CAD construction sequences are derived from [DeepCAD](https://github.com/rundiwu/DeepCAD). Programs use [CadQuery](https://github.com/CadQuery/cadquery).

## License

This dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Source materials retain their original licenses.