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In this paper, we revisit the long-standing problem of automaticreconstruction of 3D objects from single line drawings. Previousoptimization-based methods can generate compact and accurate 3D models, buttheir success rates depend heavily on the ability to (i) identifying asufficient set of true geometric constraints, and (ii) choosing a good initialvalue for the numerical optimization. In view of these challenges, we proposeto train deep neural networks to detect pairwise relationships among geometricentities (i.e., edges) in the 3D object, and to predict initial depth value ofthe vertices. Our experiments on a large dataset of CAD models show that, byleveraging deep learning in a geometric constraint solving pipeline, thesuccess rate of optimization-based 3D reconstruction can be significantlyimproved.

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