论文标题
深度方向感知功能图：解决形状匹配中的对称问题
Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching
论文作者
论文摘要
最新的非刚性形状匹配的完全固有网络通常难以消除形状的对称性，从而导致不稳定的对应性预测。同时，功能地图框架的最新进展允许通过所谓的复杂功能图，使用函数表示进行切线矢量场传输的功能表示。使用此表示形式，我们提出了一种新的深度学习方法，以在完全无监督的环境中学习方向感知功能。我们的体系结构建立在扩散网的顶部，使离散化更改变得强大。此外，我们引入了基于矢量场的损失，该损失可促进方向保存，而无需使用（通常是不稳定的）外部描述符。
State-of-the-art fully intrinsic networks for non-rigid shape matching often struggle to disambiguate the symmetries of the shapes leading to unstable correspondence predictions. Meanwhile, recent advances in the functional map framework allow to enforce orientation preservation using a functional representation for tangent vector field transfer, through so-called complex functional maps. Using this representation, we propose a new deep learning approach to learn orientation-aware features in a fully unsupervised setting. Our architecture is built on top of DiffusionNet, making it robust to discretization changes. Additionally, we introduce a vector field-based loss, which promotes orientation preservation without using (often unstable) extrinsic descriptors.
