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Choosing the right representation for geometry is crucial for making 3Dmodels compatible with existing applications. Focusing on piecewise-smoothman-made shapes, we propose a new representation that is usable in conventionalCAD modeling pipelines and can also be learned by deep neural networks. Wedemonstrate its benefits by applying it to the task of sketch-based modeling.Given a raster image, our system infers a set of parametric surfaces thatrealize the input in 3D. To capture piecewise smooth geometry, we learn aspecial shape representation: a deformable parametric template composed ofCoons patches. Naively training such a system, however, is hampered bynon-manifold artifacts in the parametric shapes and by a lack of data. Toaddress this, we introduce loss functions that bias the network to outputnon-self-intersecting shapes and implement them as part of a fullyself-supervised system, automatically generating both shape templates andsynthetic training data. We develop a testbed for sketch-based modeling,demonstrate shape interpolation, and provide comparison to related work.

Learning Manifold Patch-Based Representations of Man-Made Shapes