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.
翻译:最先进的非硬形完全内在的网络,匹配非硬体形状,往往为了消除导致不稳定通信预测的形状的对称性而挣扎。与此同时,功能地图框架最近的进展允许通过所谓的复杂功能地图,使用正切矢量的向外传输功能代表,强制进行定向保护。我们利用这种代表,提出一种新的深层次学习方法,在完全无人监督的环境中学习定向认知特征。我们的建筑建在DifpilNet之上,使其对离散变化具有活力。此外,我们引入了一个基于矢量的实地损失,促进方向保护,而不用(经常不稳定的)外部描述符。