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 (i.e., 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 significantly improved.

翻译：在本文中,我们重新审视了从单线绘图中自动重建三维天体的长期问题。 先前的优化方法可以产生精密和精确的三维模型,但其成功率在很大程度上取决于以下能力:(一) 确定一套充分真实的几何限制,以及(二) 为数字优化选择良好的初始价值。 鉴于这些挑战,我们提议对深层神经网络进行培训,以检测三维天体中几何实体(即边缘)之间的对称关系,并预测脊椎的初始深度值。 我们对大规模CAD模型数据集的实验表明,通过利用几何制约解决管道的深层学习,基于优化的三维重建的成功率可以大大提高。