Related papers: Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings

Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings

URL: http://arxiv.org/abs/2209.02692v2
Date: Wed, 7 Sep 2022 01:34:44 GMT
Title: Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings
Authors: Jia Zheng and Yifan Zhu and Kehan Wang and Qiang Zou and Zihan Zhou
Abstract summary: We propose to train deep neural networks to detect pairwise relationships among geometric entities in 3D objects. 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.
Score: 13.532686360047574
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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