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---
license: apache-2.0
task_categories:
  - text-to-3d
  - image-to-3d
tags:
  - CAD
  - CadQuery
  - synthetic-data
  - construction-sequence
  - parametric-CAD
  - 3D-generation
  - agentic-AI
  - curated
pretty_name: "Zero-to-CAD 100K"
size_categories:
  - 100K<n<1M
language:
  - en
  - code
configs:
  - config_name: default
    data_files:
      - split: train
        path: "data/train/*.parquet"
      - split: validation
        path: "data/val/*.parquet"
      - split: test
        path: "data/test/*.parquet"
---
<p align="center">
  <img src="assets/logo.png" alt="Zero-to-CAD" width="100%"/>
</p>

# Zero-to-CAD 100K

**A curated subset of 100,000 geometrically diverse CAD construction sequences selected from Zero-to-CAD 1M.**

<p align="center">
  <img src="assets/agentic.png" alt="Zero-to-CAD agentic synthesis pipeline" width="800"/>
</p>

> **Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data**
>
> [Mohammadmehdi Ataei](https://orcid.org/0000-0002-3399-9696), [Farzaneh Askari](https://orcid.org/0000-0003-0684-1102), [Kamal Rahimi Malekshan](https://orcid.org/0009-0004-1192-4724), [Pradeep Kumar Jayaraman](https://orcid.org/0000-0001-6314-6136)
>
> Autodesk Research

## Related Resources

| Resource | Link |
|----------|------|
| 📄 **Paper** | [Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data](https://arxiv.org/abs/2604.24479) |
| 📦 **Zero-to-CAD 1M** (full dataset) | [ADSKAILab/Zero-To-CAD-1m](https://huggingface.co/datasets/ADSKAILab/Zero-To-CAD-1m) |
| 📦 **Zero-to-CAD 100K** (this dataset) | You are here |
| 🤖 **Fine-tuned Model** (Qwen3-VL-2B) | [ADSKAILab/Zero-To-CAD-Qwen3-VL-2B](https://huggingface.co/ADSKAILab/Zero-To-CAD-Qwen3-VL-2B) |
| 🗂️ **Collection** | [ADSKAILab/Zero-To-CAD](https://huggingface.co/collections/ADSKAILab/zero-to-cad) |

## Overview

This is the **curated 100K subset** of Zero-to-CAD, designed as an accessible entry point for researchers working with limited compute. The samples are selected for **maximum geometric diversity** from the full 1M dataset.

### Curation Process

1. **Visual embedding**: Each model is rendered from 8 viewpoints and encoded using DINOv3 features, averaged across views.
2. **Clustering**: K-means clustering partitions the embedding space into 100K clusters.
3. **Selection**: The nearest-to-centroid exemplar from each cluster is selected.

This ensures the subset spans the full distribution of part types, operations, and geometric complexity present in the 1M dataset.

<p align="center">
  <img src="assets/samples.png" alt="Sample CAD models from Zero-to-CAD" width="100%"/>
</p>

## When to Use This vs. the 1M Dataset

| Use case | Recommended |
|----------|-------------|
| Quick prototyping & experimentation | ✅ **100K** |
| Training large models | 📦 1M |
| Benchmarking & evaluation | ✅ **100K** |
| Resource-constrained environments | ✅ **100K** |
| Maximum training data coverage | 📦 1M |

## Dataset Details

### Splits

| Split | Samples |
|-------|---------|
| Train | 81,015 |
| Validation | 9,734 |
| Test | 9,767 |

### Data Fields

Each sample contains:

| Field | Type | Description |
|-------|------|-------------|
| `uuid` | `string` | Unique identifier (matches 1M dataset) |
| `cadquery_file` | `string` | Executable CadQuery Python source code |
| `num_faces` | `int` | Number of B-Rep faces in the final solid |
| `face_latency_ms` | `float` | Time to compute face count (ms) |
| `cadquery_ops_json` | `string` | JSON list of CAD operations used |
| `cadquery_ops_count` | `int` | Number of CAD operations in the construction sequence |
| `ops_latency_ms` | `float` | Time to extract operations (ms) |
| `num_renders` | `int` | Number of rendered views |
| `image_0` – `image_7` | `image` | 8 rendered views (256×256) |
| `stl_file` | `bytes` | Exported STL mesh |
| `step_file` | `bytes` | Exported STEP file |

### CAD Operations Coverage

Broad operation vocabulary identical to the 1M dataset:

- **Sketch primitives**: rect, circle, polygon, arc, spline, slot
- **3D operations**: extrude, cut, revolve, loft, sweep
- **Modifications**: fillet, chamfer, shell, offset
- **Booleans**: union, cut, intersect
- **Patterns**: linear, polar, mirror
- **Features**: holes (through, blind, countersink), threads, ribs

## Quick Start

### Load the dataset

```python
from datasets import load_dataset
# Streaming mode — rows are fetched on demand
ds = load_dataset("ADSKAILab/Zero-To-CAD-100k")
# Get a single sample
sample = next(iter(ds))
# Display the reconstructed script
cad_code = bytes(sample["cadquery_file"]).decode("utf-8")
print(cad_code)
```

### Execute a sample

```python
import cadquery as cq
# Streaming mode — rows are fetched on demand
ds = load_dataset("ADSKAILab/Zero-To-CAD-100k", split="train", streaming=True)
sample = next(iter(ds))
# Execute the code from a sample
code = bytes(sample["cadquery_file"]).decode("utf-8")
exec(code)
# Display generated CadQuery solid
from IPython.display import display
display(result)
```

## Embeddings & FAISS Index

Precomputed DINOv3 embeddings, a FAISS IVF-PQ index, and precomputed nearest neighbors for the full 1M dataset are available in the [1M dataset repository](https://huggingface.co/datasets/ADSKAILab/Zero-To-CAD-1m) under `embeddings/`. These cover all 1M samples, including every sample in this 100K subset.

The `cad_gen_diverse_samples.csv` file in that folder documents the clustering-based selection process used to curate this subset (cluster IDs, distances to centroids, etc.).

```python
import faiss
from huggingface_hub import hf_hub_download
# Download the FAISS index from the 1M repo
index_path = hf_hub_download("ADSKAILab/Zero-To-CAD-1m", "embeddings/cad_gen_ivfpq.index", repo_type="dataset")
index = faiss.read_index(index_path)
```

See the [1M dataset card](https://huggingface.co/datasets/ADSKAILab/Zero-To-CAD-1m#embeddings--faiss-index) for full usage examples.

## Intended Uses

- **Quick prototyping** of CAD generation models with a manageable dataset size
- **Evaluation & benchmarking** with a representative, diverse sample
- **Fine-tuning smaller models** when full 1M training is not feasible

## Citation

If you use this dataset, please cite:

```bibtex
@misc{ataei2026zerotocadagenticsynthesisinterpretable,
  title={Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data}, 
  author={Mohammadmehdi Ataei and Farzaneh Askari and Kamal Rahimi Malekshan and Pradeep Kumar Jayaraman},
  year={2026},
  eprint={2604.24479},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2604.24479}
}
```

## License

This dataset is released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

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	---
	license: apache-2.0
	task_categories:
	- text-to-3d
	- image-to-3d
	tags:
	- CAD
	- CadQuery
	- synthetic-data
	- construction-sequence
	- parametric-CAD
	- 3D-generation
	- agentic-AI
	- curated
	pretty_name: "Zero-to-CAD 100K"
	size_categories:
	- 100K<n<1M
	language:
	- en
	- code
	configs:
	- config_name: default
	data_files:
	- split: train
	path: "data/train/*.parquet"
	- split: validation
	path: "data/val/*.parquet"
	- split: test
	path: "data/test/*.parquet"
	---

	<p align="center">
	<img src="assets/logo.png" alt="Zero-to-CAD" width="100%"/>
	</p>

	# Zero-to-CAD 100K

	A curated subset of 100,000 geometrically diverse CAD construction sequences selected from Zero-to-CAD 1M.

	<p align="center">
	<img src="assets/agentic.png" alt="Zero-to-CAD agentic synthesis pipeline" width="800"/>
	</p>

	> Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data
	>
	> [Mohammadmehdi Ataei](https://orcid.org/0000-0002-3399-9696), [Farzaneh Askari](https://orcid.org/0000-0003-0684-1102), [Kamal Rahimi Malekshan](https://orcid.org/0009-0004-1192-4724), [Pradeep Kumar Jayaraman](https://orcid.org/0000-0001-6314-6136)
	>
	> Autodesk Research

	## Related Resources

	\| Resource \| Link \|
	\|----------\|------\|
	\| 📄 Paper \| [Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data](https://arxiv.org/abs/2604.24479) \|
	\| 📦 Zero-to-CAD 1M (full dataset) \| [ADSKAILab/Zero-To-CAD-1m](https://huggingface.co/datasets/ADSKAILab/Zero-To-CAD-1m) \|
	\| 📦 Zero-to-CAD 100K (this dataset) \| You are here \|
	\| 🤖 Fine-tuned Model (Qwen3-VL-2B) \| [ADSKAILab/Zero-To-CAD-Qwen3-VL-2B](https://huggingface.co/ADSKAILab/Zero-To-CAD-Qwen3-VL-2B) \|
	\| 🗂️ Collection \| [ADSKAILab/Zero-To-CAD](https://huggingface.co/collections/ADSKAILab/zero-to-cad) \|

	## Overview

	This is the curated 100K subset of Zero-to-CAD, designed as an accessible entry point for researchers working with limited compute. The samples are selected for maximum geometric diversity from the full 1M dataset.

	### Curation Process

	1. Visual embedding: Each model is rendered from 8 viewpoints and encoded using DINOv3 features, averaged across views.
	2. Clustering: K-means clustering partitions the embedding space into 100K clusters.
	3. Selection: The nearest-to-centroid exemplar from each cluster is selected.

	This ensures the subset spans the full distribution of part types, operations, and geometric complexity present in the 1M dataset.

	<p align="center">
	<img src="assets/samples.png" alt="Sample CAD models from Zero-to-CAD" width="100%"/>
	</p>

	## When to Use This vs. the 1M Dataset

	\| Use case \| Recommended \|
	\|----------\|-------------\|
	\| Quick prototyping & experimentation \| ✅ 100K \|
	\| Training large models \| 📦 1M \|
	\| Benchmarking & evaluation \| ✅ 100K \|
	\| Resource-constrained environments \| ✅ 100K \|
	\| Maximum training data coverage \| 📦 1M \|

	## Dataset Details

	### Splits

	\| Split \| Samples \|
	\|-------\|---------\|
	\| Train \| 81,015 \|
	\| Validation \| 9,734 \|
	\| Test \| 9,767 \|

	### Data Fields

	Each sample contains:

	\| Field \| Type \| Description \|
	\|-------\|------\|-------------\|
	\| `uuid` \| `string` \| Unique identifier (matches 1M dataset) \|
	\| `cadquery_file` \| `string` \| Executable CadQuery Python source code \|
	\| `num_faces` \| `int` \| Number of B-Rep faces in the final solid \|
	\| `face_latency_ms` \| `float` \| Time to compute face count (ms) \|
	\| `cadquery_ops_json` \| `string` \| JSON list of CAD operations used \|
	\| `cadquery_ops_count` \| `int` \| Number of CAD operations in the construction sequence \|
	\| `ops_latency_ms` \| `float` \| Time to extract operations (ms) \|
	\| `num_renders` \| `int` \| Number of rendered views \|
	\| `image_0` – `image_7` \| `image` \| 8 rendered views (256×256) \|
	\| `stl_file` \| `bytes` \| Exported STL mesh \|
	\| `step_file` \| `bytes` \| Exported STEP file \|

	### CAD Operations Coverage

	Broad operation vocabulary identical to the 1M dataset:

	- Sketch primitives: rect, circle, polygon, arc, spline, slot
	- 3D operations: extrude, cut, revolve, loft, sweep
	- Modifications: fillet, chamfer, shell, offset
	- Booleans: union, cut, intersect
	- Patterns: linear, polar, mirror
	- Features: holes (through, blind, countersink), threads, ribs

	## Quick Start

	### Load the dataset

	```python
	from datasets import load_dataset

	# Streaming mode — rows are fetched on demand
	ds = load_dataset("ADSKAILab/Zero-To-CAD-100k")

	# Get a single sample
	sample = next(iter(ds))

	# Display the reconstructed script
	cad_code = bytes(sample["cadquery_file"]).decode("utf-8")
	print(cad_code)
	```

	### Execute a sample

	```python
	import cadquery as cq

	# Streaming mode — rows are fetched on demand
	ds = load_dataset("ADSKAILab/Zero-To-CAD-100k", split="train", streaming=True)
	sample = next(iter(ds))

	# Execute the code from a sample
	code = bytes(sample["cadquery_file"]).decode("utf-8")
	exec(code)

	# Display generated CadQuery solid
	from IPython.display import display
	display(result)
	```

	## Embeddings & FAISS Index

	Precomputed DINOv3 embeddings, a FAISS IVF-PQ index, and precomputed nearest neighbors for the full 1M dataset are available in the [1M dataset repository](https://huggingface.co/datasets/ADSKAILab/Zero-To-CAD-1m) under `embeddings/`. These cover all 1M samples, including every sample in this 100K subset.

	The `cad_gen_diverse_samples.csv` file in that folder documents the clustering-based selection process used to curate this subset (cluster IDs, distances to centroids, etc.).

	```python
	import faiss
	from huggingface_hub import hf_hub_download

	# Download the FAISS index from the 1M repo
	index_path = hf_hub_download("ADSKAILab/Zero-To-CAD-1m", "embeddings/cad_gen_ivfpq.index", repo_type="dataset")
	index = faiss.read_index(index_path)
	```

	See the [1M dataset card](https://huggingface.co/datasets/ADSKAILab/Zero-To-CAD-1m#embeddings--faiss-index) for full usage examples.

	## Intended Uses

	- Quick prototyping of CAD generation models with a manageable dataset size
	- Evaluation & benchmarking with a representative, diverse sample
	- Fine-tuning smaller models when full 1M training is not feasible

	## Citation

	If you use this dataset, please cite:

	```bibtex
	@misc{ataei2026zerotocadagenticsynthesisinterpretable,
	title={Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data},
	author={Mohammadmehdi Ataei and Farzaneh Askari and Kamal Rahimi Malekshan and Pradeep Kumar Jayaraman},
	year={2026},
	eprint={2604.24479},
	archivePrefix={arXiv},
	primaryClass={cs.CV},
	url={https://arxiv.org/abs/2604.24479}
	}
	```

	## License

	This dataset is released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).