  Low isometric distortion is often required for mesh parameterizations. A
configuration of some vertices, where the distortion is concentrated, provides
a way to mitigate isometric distortion, but determining the number and
placement of these vertices is non-trivial. We call these vertices distortion
points. We present a novel and automatic method to detect distortion points
using a voting strategy. Our method integrates two components: candidate
generation and candidate voting. Given a closed triangular mesh, we generate
candidate distortion points by executing a three-step procedure repeatedly: (1)
randomly cut an input to a disk topology; (2) compute a low conformal
distortion parameterization; and (3) detect the distortion points. Finally, we
count the candidate points and generate the final distortion points by voting.
We demonstrate that our algorithm succeeds when employed on various closed
meshes with a genus of zero or higher. The distortion points generated by our
method are utilized in three applications, including planar parameterization,
semi-automatic landmark correspondence, and isotropic remeshing. Compared to
other state-of-the-art methods, our method demonstrates stronger practical
robustness in distortion point detection.
