论文标题
DA魔杖：使用神经网格参数化选择失真感知
DA Wand: Distortion-Aware Selection using Neural Mesh Parameterization
论文作者
论文摘要
我们提出了一种神经技术，用于学习在一个可用于网格参数化的点附近选择局部子区域。我们框架的动机是由用于表面上贴花，纹理或绘画的交互式工作流程所驱动的。我们的关键思想是将分割概率合并为经典参数化方法的权重，该方法在神经网络框架内实现为新的可区分参数化层。我们训练一个分割网络，以选择被参数化为2D的3D区域，并因结果失真而受到惩罚，从而产生失真感知的细分。训练后，用户可以使用我们的系统在网格上进行交互选择点，并在选择周围获得一个较大的，有意义的区域，从而诱导低衰弱的参数化。我们的代码和项目页面当前可用。
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.
