Modelpublic

introvoyz041/Zero-To-CAD-Qwen3-VL-2B

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

0likes4downloads

Model Card

Zero-to-CAD — Qwen3-VL-2B

A vision-language model fine-tuned to reconstruct executable CAD programs from multi-view images.

Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data Mohammadmehdi Ataei, Farzaneh Askari, Kamal Rahimi Malekshan, Pradeep Kumar Jayaraman Autodesk Research

Related Resources

Resource	Link
📄 Paper	Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data
📦 Zero-to-CAD 1M (full dataset)	ADSKAILab/Zero-To-CAD-1m
📦 Zero-to-CAD 100K (curated subset)	ADSKAILab/Zero-To-CAD-100k
🤖 Fine-tuned Model (this model)	You are here
🗂️ Collection	ADSKAILab/Zero-To-CAD

Model Description

This model is a fully fine-tuned Qwen3-VL-2B-Instruct that takes 8 rendered views of a 3D shape (4 front, 4 rear at 256×256) and generates executable CadQuery Python code that reproduces the geometry.

The model was trained entirely on synthetic data from Zero-to-CAD 1M (979,633 training samples) — no real-world CAD files were used.

Key Results

Benchmark	Success Rate	Mean IoU	Median IoU	P90 IoU
Zero-to-CAD test	82.1%	0.747	0.847	0.999
ABC (out-of-distribution)	61.0%	0.377	0.303	0.854

Comparison with Baselines

Model	Zero-to-CAD Success	Zero-to-CAD Mean IoU	ABC Success	ABC Mean IoU
This model	82.1%	0.747	61.0%	0.377
GPT-5.2 High	72.2%	0.485	66.2%	0.344
GPT-5.2 Medium	71.1%	0.495	62.6%	0.346
Qwen3-VL-2B (base)	6.6%	0.184	5.4%	0.131

Quick Start

Inference

python

from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from datasets import load_dataset
from PIL import Image
import io


model_name = "ADSKAILab/Zero-To-CAD-Qwen3-VL-2B"
model = Qwen3VLForConditionalGeneration.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained(model_name)

# Load 8 rendered views from the dataset
ds = load_dataset("ADSKAILab/Zero-To-CAD-1m", split="train", streaming=True)
sample = next(iter(ds))
views = [
    Image.open(io.BytesIO(sample[f"image_{i}"])) if isinstance(sample[f"image_{i}"], bytes)
    else sample[f"image_{i}"]
    for i in range(8)
]

# Or load 8 views from local files:
# views = [Image.open(f"view_{i}.png") for i in range(8)]

messages = [
    {
        "role": "system",
        "content": "You are a CAD code assistant. Given multiple rendered views of a 3D shape, generate clean, well-structured CadQuery Python code that accurately reproduces the geometry."
    },
    {
        "role": "user",
        "content": [
            *[{"type": "image", "image": view} for view in views],
            {"type": "text", "text": "Generate CadQuery code for this shape."}
        ]
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, images=views, return_tensors="pt").to(model.device)

output_ids = model.generate(**inputs, max_new_tokens=4096)
output_text = processor.batch_decode(output_ids[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0]

print(output_text)

Execute the generated code

python

import cadquery as cq

exec(output_text)
# `result` contains the reconstructed CadQuery solid

# Export
cq.exporters.export(result, "output.step")
cq.exporters.export(result, "output.stl")

Training Details

Hyperparameter	Value
Base model	Qwen3-VL-2B-Instruct
Training mode	Full fine-tuning
Max sequence length	4,096 tokens
Optimizer	AdamW
Learning rate	1 × 10⁻⁴
Weight decay	0.0
LR scheduler	Cosine
Warmup ratio	0.03
Attention dropout	0.1
GPUs	16 × NVIDIA H100 80GB
Per-GPU batch size	1
Effective batch size	16
Epochs	3
Precision	bfloat16
Distributed strategy	DDP

Evaluation Protocol

—Metric: Voxelized IoU at 64³ resolution between generated and ground-truth solids
—Rotational alignment: Maximum IoU over 45° rotation increments
—Success rate: Percentage of generations producing valid, executable CadQuery code

Intended Uses

—Image-to-CAD reconstruction — reconstruct editable parametric CAD from rendered views
—Research baseline — starting point for Image-to-Sequence CAD generation research
—Integration — combine with rendering pipelines for end-to-end 3D reconstruction

Limitations

—Trained on synthetic data only; may struggle with photorealistic or noisy inputs
—Expects 8 clean rendered views at 256×256 — other configurations are untested
—Outputs CadQuery code only; other CAD formats require post-processing
—Complex multi-part assemblies may exceed the 4,096 token context window

Citation

If you use this model, 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 model is released under the Apache License 2.0.