Inferring 3D shapes and deformations from single views
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Single and sparse view 3D reconstruction by learning shape priors
Computer Vision and Image Understanding
Exploiting artistic cues to obtain line labels for free-hand sketches
Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling
Inferring mirror symmetric 3D shapes from sketches
Computer-Aided Design
Reconstruction of relief objects from archeological line drawings
Journal on Computing and Cultural Heritage (JOCCH)
Precise 3d reconstruction from a single image
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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In previous optimization-based methods of 3D planar-faced object reconstruction from single 2D line drawings, the missing depths of the vertices of a line drawing (and other parameters in some methods) are used as the variables of the objective functions. A 3D object with planar faces is derived by finding values for these variables that minimize the objective functions. These methods work well for simple objects with a small number N of variables. As N grows, however, it is very difficult for them to find expected objects. This is because with the nonlinear objective functions in a space of large dimension N, the search for optimal solutions can easily get trapped into local minima. In this paper, we use the parameters of the planes that pass through the planar faces of an object as the variables of the objective function. This leads to a set of linear constraints on the planes of the object, resulting in a much lower dimensional nullspace where optimization is easier to achieve. We prove that the dimension of this nullspace is exactly equal to the minimum number of vertex depths which define the 3D object. Since a practical line drawing is usually not an exact projection of a 3D object, we expand the nullspace to a larger space based on the singular value decomposition of the projection matrix of the line drawing. In this space, robust 3D reconstruction can be achieved. Compared with two most related methods, our method not only can reconstruct more complex 3D objects from 2D line drawings, but also is computationally more efficient.