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<h1 class="title is-2 publication-title">From 2D CAD Drawings to 3D Parametric Models: A Vision-Language Approach</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
Xilin Wang<sup>1*</sup></span>
<span class="author-block">
<a href="https://bertjiazheng.github.io">Jia Zheng</a><sup>2*</sup></span>
<span class="author-block">
Yuanchao Hu<sup>2</sup></span>
<span class="author-block">
Hao Zhu<sup>2</sup></span>
<span class="author-block">
<a href="https://yuqian1023.github.io">Qian Yu</a><sup>1†</sup></span>
<span class="author-block">
<a href="https://zihan-z.github.io">Zihan Zhou</a><sup>2†</sup>
</span>
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<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Beihang University</span>
<span class="author-block"><sup>2</sup><a href="https://kujiale.com">Manycore Tech Inc.</a></span>
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<span class="author-block">*Equal contribution</span>
<span class="author-block">*Corresponding authors</span>
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<span c lass="dnerf">CAD2Program</span> is a Vision-Language Model for reconstructing 3D parametric models from 2D CAD drawings.
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<p>
In this paper, we present <span c lass="dnerf">CAD2Program</span>, a new method for reconstructing 3D parametric models from 2D CAD drawings. Our proposed method is inspired by recent successes in vision-language models (VLMs), and departs from traditional methods which rely on task-specific data representations and/or algorithms. Specifically, on the input side, we simply treat the 2D CAD drawing as a raster image, regardless of its original format, and encode the image with a standard ViT model. We show that such an encoding scheme achieves competitive performance against existing methods that operate on vector-graphics inputs, while imposing substantially fewer restrictions on the 2D drawings. On the output side, our method auto-regressively predicts a general-purpose language describing 3D parametric models in text form. Compared to other sequence modeling methods for CAD which use domain-specific sequence representations with fixed-size slots, our text-based representation is more flexible, and can be easily extended to arbitrary geometric entities and semantic or functional properties. Experimental results on a large-scale dataset of cabinet models demonstrate the effectiveness of our method.
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<h2 class="title is-3">2D CAD Drawings</h2>
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<p>
An engineering drawing is mixture of two types of layers:
<ul>
<li><em>geometry layer</em>, which is the actual object described by its orthographic projections,</li>
<li><em>annotation layer</em>, which includes dimensioning and function symbols, such as surface types, manufacturing instructions, <em>etc.</em></li>
</ul>
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<h2 class="title is-3">3D Parametric Models</h2>
<img src="static/images/parameters.png" width="80%">
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<p>
In this paper, a 3D cabinet is built by assembling pre-defined primitive models. Each primitive instance is defined by a computer program, which consists of three parts:
<ul>
<li><em>model ID</em>, which is a unique identifier of a primitive in the database,</li>
<li><em>common parameters</em>, which indicate the general pose and size of the primitive in the 3D space,</li>
<li><em>model-specific parameters</em>, which describe possible variations of a specific primitive.</li>
</ul>
</p>
<p>
We represent 3D parametric models as scripts of a general-purpose language (<em>e.g.</em>, Python). The shape program of the above cabinet is shown as follows:
</p>
<pre style="font-size: 12px"><code>bbox_0 = Bbox(507, 185, 805, 1014, 370, 50, 0)
model_0 = <model_57761062>()
bbox_1 = Bbox(25, 185, 390, 50, 370, 780, 0)
model_1 = <model_57758898>()
bbox_2 = Bbox(532, 195, 390, 964, 350, 780, 0)
model_2 = <model_115813862>(N=1, NKA=928, DBXX=1, BT=18)
bbox_3 = Bbox(532, 185, 390, 928, 330, 18, 0)
model_3 = <model_57253481>()
bbox_4 = Bbox(291, 11, 390, 478, 18, 776, 0)
model_4 = <model_82289390>(openDirection=0, uCove=18, dCover=18, lCover=18, rCover=18)
bbox_5 = Bbox(773, 11, 390, 478, 18, 776, 0)
model_5 = <model_82289390>(openDirection=1, uCover=18, dCover=18, lCover=18, rCover=18)
</code></pre>
<p>
The above script defines a cabinet with six primitive models. Each two lines corresponds to a primitive model. The odd line defined the bounding box of the primitive and then the even line defined the model ID and associated parameters.
</p>
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<h2 class="title is-3">CAD2Program Model</h2>
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<p>
We adopt an off-the-shelf Vision-Language Model (such as InternVL). The <span c lass="dnerf">CAD2Program</span> takes 2D engineering drawing as input and outputs a text-form of shape program, which depicts the 3D parametric model. The pipeline of our method is shown as follows.
</p>
</div>
<img src="static/images/internvl.png" width="80%">
<div class="content has-text-justified">
<p>
We show a conversation example of prompt and response in Python format in the following.
</p>
</div>
<img src="static/images/conversation.png" width="80%">
</div>
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<!--/ Model. -->
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<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{CAD2Program,
author = {Wang, Xilin, Zheng, Jia and Hu, Yuanchao and Zhu, Hao and Yu, Qian and Zhou, Zihan},
title = {From 2D CAD Drawings to 3D Parametric Models: A Vision-Language Approach},
booktitle = {AAAI},
year = {2025}
}</code></pre>
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<section class="section" id="Acknowledgements">
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<h2 class="title">Acknowledgements</h2>
This work was done during Xilin Wang's internship at <a href="https://kujiale.com">Manycore Tech Inc.</a>
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