TY - GEN
T1 - The shape space of discrete orthogonal geodesic nets
AU - Rabinovich, Michael
AU - Hoffmann, Tim
AU - Sorkine-Hornung, Olga
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s).
PY - 2018/12/4
Y1 - 2018/12/4
N2 - Discrete orthogonal geodesic nets (DOGs) are a quad mesh analogue of developable surfaces. In this work we study continuous deformations on these discrete objects. Our main theoretical contribution is the characterization of the shape space of DOGs for a given net connectivity. We show that generally, this space is locally a manifold of a fixed dimension, apart from a set of singularities, implying that DOGs are continuously deformable. Smooth flows can be constructed by a smooth choice of vectors on the manifold's tangent spaces, selected to minimize a desired objective function under a given metric. We show how to compute such vectors by solving a linear system, and we use our findings to devise a geometrically meaningful way to handle singular points. We base our shape space metric on a novel DOG Laplacian operator, which is proved to converge under sampling of an analytical orthogonal geodesic net. We further show how to extend the shape space of DOGs by supporting creases and curved folds and apply the developed tools in an editing system for developable surfaces that supports arbitrary bending, stretching, cutting, (curved) folds, as well as smoothing and subdivision operations.
AB - Discrete orthogonal geodesic nets (DOGs) are a quad mesh analogue of developable surfaces. In this work we study continuous deformations on these discrete objects. Our main theoretical contribution is the characterization of the shape space of DOGs for a given net connectivity. We show that generally, this space is locally a manifold of a fixed dimension, apart from a set of singularities, implying that DOGs are continuously deformable. Smooth flows can be constructed by a smooth choice of vectors on the manifold's tangent spaces, selected to minimize a desired objective function under a given metric. We show how to compute such vectors by solving a linear system, and we use our findings to devise a geometrically meaningful way to handle singular points. We base our shape space metric on a novel DOG Laplacian operator, which is proved to converge under sampling of an analytical orthogonal geodesic net. We further show how to extend the shape space of DOGs by supporting creases and curved folds and apply the developed tools in an editing system for developable surfaces that supports arbitrary bending, stretching, cutting, (curved) folds, as well as smoothing and subdivision operations.
KW - Developable surfaces
KW - Discrete differential geometry
KW - Geodesic nets
KW - Shape modeling
KW - Shape space
UR - http://www.scopus.com/inward/record.url?scp=85066062754&partnerID=8YFLogxK
U2 - 10.1145/3272127.3275088
DO - 10.1145/3272127.3275088
M3 - Conference contribution
AN - SCOPUS:85066062754
T3 - SIGGRAPH Asia 2018 Technical Papers, SIGGRAPH Asia 2018
BT - SIGGRAPH Asia 2018 Technical Papers, SIGGRAPH Asia 2018
PB - Association for Computing Machinery, Inc
T2 - SIGGRAPH Asia 2018 Technical Papers - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH Asia 2018
Y2 - 4 December 2018 through 7 December 2018
ER -