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FlowRep : extracting descriptive curve networks from free-form design shapes

University of British Columbia

This thesis presents FlowRep, an algorithm for extracting descriptive compact 3D curve networks from meshes of free-form man-made shapes. FlowRep output networks provide a concise visual description of the underlying surface, and can be used as a compact proxy for shape compression, editing and manipulation. While artists routinely and successfully create descriptive curve networks to depict complex 3D shapes in 3D space or on 2D media, the method described here is the first to achieve this goal algorithmically. FlowRep infers the desired compact curve network from complex 3D geometries by using a series of insights derived from perception, computer graphics, and design literature which point to two sets of geometric properties that such networks should satisfy. These sources suggest that visually descriptive networks are cycle-descriptive, i.e their cycles unambiguously describe the geometry of the surface patches they surround. They also indicate that such networks are designed to be projectable, or easy to envision when observed from a static general viewpoint; in other words, 2D projections of the network should be strongly indicative of its 3D geometry. Research suggests that both properties are best achieved by using networks dominated by flowlines, surface curves aligned with principal curvature directions across anisotropic regions and strategically extended across sharp-features and isotropic areas. The algorithm leverages these observations in the construction of a compact descriptive curve network. Starting with a curvature aligned quad dominant mesh I first extract sequences of mesh edges that form long, well-shaped and reliable flowlines by leveraging directional similarity between nearby meaningful flowline directions. This process overcomes topological noise, and inaccuracies and singularities in the underlying curvature field. I then use the extracted flowlines and the model's sharp-feature, or trim, curves to form a projectable network which describes the underlying surface. Finally, I simplify this network while preserving its descriptive power to obtain the final result. My co-authors and I validate our method by demonstrating a range of networks computed from diverse inputs, using them for surface reconstruction, and showing extensive comparisons with prior work and artist generated networks.

Text

2017-08-10

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/62558

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/