TY - GEN
T1 - Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing
AU - Le Folgoc, Loïc
AU - Castro, Daniel C.
AU - Tan, Jeremy
AU - Khanal, Bishesh
AU - Kamnitsas, Konstantinos
AU - Walker, Ian
AU - Alansary, Amir
AU - Glocker, Ben
PY - 2019/5/22
Y1 - 2019/5/22
N2 - We investigate discrete spin transformations, a geometric framework to manipulate surface meshes by controlling mean curvature. Applications include surface fairing – flowing a mesh onto say, a reference sphere – and mesh extrusion – e.g., rebuilding a complex shape from a reference sphere and curvature specification. Because they operate in curvature space, these operations can be conducted very stably across large deformations with no need for remeshing. Spin transformations add to the algorithmic toolbox for pose-invariant shape analysis. Mathematically speaking, mean curvature is a shape invariant and in general fully characterizes closed shapes (together with the metric). Computationally speaking, spin transformations make that relationship explicit. Our work expands on a discrete formulation of spin transformations. Like their smooth counterpart, discrete spin transformations are naturally close to conformal (angle-preserving). This quasi-conformality can nevertheless be relaxed to satisfy the desired trade-off between area distortion and angle preservation. We derive such constraints and propose a formulation in which they can be efficiently incorporated. The approach is showcased on subcortical structures.
AB - We investigate discrete spin transformations, a geometric framework to manipulate surface meshes by controlling mean curvature. Applications include surface fairing – flowing a mesh onto say, a reference sphere – and mesh extrusion – e.g., rebuilding a complex shape from a reference sphere and curvature specification. Because they operate in curvature space, these operations can be conducted very stably across large deformations with no need for remeshing. Spin transformations add to the algorithmic toolbox for pose-invariant shape analysis. Mathematically speaking, mean curvature is a shape invariant and in general fully characterizes closed shapes (together with the metric). Computationally speaking, spin transformations make that relationship explicit. Our work expands on a discrete formulation of spin transformations. Like their smooth counterpart, discrete spin transformations are naturally close to conformal (angle-preserving). This quasi-conformality can nevertheless be relaxed to satisfy the desired trade-off between area distortion and angle preservation. We derive such constraints and propose a formulation in which they can be efficiently incorporated. The approach is showcased on subcortical structures.
UR - http://www.scopus.com/inward/record.url?scp=85066120787&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-20351-1_17
DO - 10.1007/978-3-030-20351-1_17
M3 - Conference contribution
SN - 9783030203504
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 221
EP - 234
BT - Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings
A2 - Bao, Siqi
A2 - Gee, James C.
A2 - Yushkevich, Paul A.
A2 - Chung, Albert C.S.
ER -