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.

翻译：我们提出一种神经技术，用于学习选取一个点周围的局部子区域，以实现网格参数化。该框架的动机源于用于曲面贴花、纹理绘制或涂色的交互式工作流程。我们的核心思想是将分割概率作为经典参数化方法的权重，并通过神经网络框架内实现的新型可微分参数化层来实施。我们训练一个分割网络，用于选取三维区域，这些区域被参数化为二维，并根据产生的失真施加惩罚，从而形成具有失真感知能力的分割。训练完成后，用户可利用我们的系统交互式地选取网格上的一个点，并获得一个围绕该点的大范围、有意义、且能诱导低失真参数化的区域。我们的代码和项目页面目前已经公开。