| --- |
| 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. |
|
|
