TY - JOUR
T1 - The shape space of discrete orthogonal geodesic nets
AU - Rabinovich, Michael
AU - Hoffmann, Tim
AU - Sorkine-Hornung, Olga
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/11
Y1 - 2018/11
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 fiows 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 fiows 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 modelling
KW - Shape space
UR - http://www.scopus.com/inward/record.url?scp=85061985023&partnerID=8YFLogxK
U2 - 10.1145/3272127.3275088
DO - 10.1145/3272127.3275088
M3 - Article
AN - SCOPUS:85061985023
SN - 0730-0301
VL - 37
JO - ACM Transactions on Graphics
JF - ACM Transactions on Graphics
IS - 6
M1 - 228
ER -