We present a neural technique for learning to select a local sub-region
around a point which can be used for mesh parameterization. The motivation for
our framework is driven by interactive workflows used for decaling, texturing,
or painting on surfaces. Our key idea is to incorporate segmentation
probabilities as weights of a classical parameterization method, implemented as
a novel differentiable parameterization layer within a neural network
framework. We train a segmentation network to select 3D regions that are
parameterized into 2D and penalized by the resulting distortion, giving rise to
segmentations which are distortion-aware. Following training, a user can use
our system to interactively select a point on the mesh and obtain a large,
meaningful region around the selection which induces a low-distortion
parameterization. Our code and project page are currently available.Comment: Project page: https://threedle.github.io/DA-Wand/ Code:
https://github.com/threedle/DA-Wan