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<!DOCTYPE html>
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<meta property="og:title" content="Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings." />
<meta name="keywords" content="Constraint Detection, Geometric Constraint Solving, Wireframe, Line Drawing, 3D Reconstruction">
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<title>Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings</title>
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<h1 class="title is-2 publication-title">Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings</h1>
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<a href="https://bertjiazheng.github.io">Jia Zheng</a><sup>1</sup></span>
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<a href="https://stevenzzz07.github.io">Yifan Zhu</a><sup>2</sup></span>
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<a href="https://jason-khan.github.io">Kehan Wang</a><sup>3</sup></span>
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<a href="https://qiang-zou.github.io">Qiang Zou</a><sup>4</sup></span>
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<a href="https://zihan-z.github.io">Zihan Zhou</a><sup>1</sup>
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<span class="author-block"><sup>1</sup><a href="https://kujiale.com">Manycore Tech Inc.</a></span>
<span class="author-block"><sup>2</sup>Nanjing University of Aeronautics and Astronautics</span>
<span class="author-block"><sup>3</sup>University of California, Berkeley</span>
<span class="author-block"><sup>4</sup>State Key Lab of CAD&CG, Zhejiang University </span>
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Given (a) an input line drawing, we train deep models to predict (b) geometric constraints (<em>e.g.</em>, parallel constraints) and (c) initial depth value of the vertices, which are then used in numerical optimization for geometric constraint solving to obtain an accurate and compact 3D model (d).
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In this paper, we revisit the long-standing problem of automatic reconstruction of 3D objects from single line drawings. Previous optimization-based methods can generate compact and accurate 3D models, but their success rates depend heavily on the ability to (i) identifying a sufficient set of true geometric constraints, and (ii) choosing a good initial value for the numerical optimization. In view of these challenges, we propose to train deep neural networks to detect pairwise relationships among geometric entities (<em>i.e.</em>, edges) in the 3D object, and to predict initial depth value of the vertices. Our experiments on a large dataset of CAD models show that, by leveraging deep learning in a geometric constraint solving pipeline, the success rate of optimization-based 3D reconstruction can be drastically improved.
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<pre><code>@article{cstr,
author = {Zheng, Jia and Zhu, Yifan and Wang, Kehan and Zou, Qiang and Zhou, Zihan},
title = {Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings},
journal = {CoRR},
volume = {abs/2209.02692},
year = {2022},
}</code></pre>
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<h2 class="title">Acknowledgements</h2>
This work was done during Yifan Zhu and Kehan Wang's internships at Manycore Tech Inc. This work was supported in part by the Key R&D Program of Zhejiang Province (No. 2022C01025), the National Natural Science Foundation of China (No. 62102355), the Natural Science Foundation of Zhejiang Province (No. LQ22F020012).
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