论文标题
弱监督的深度功能图以匹配形状
Weakly Supervised Deep Functional Map for Shape Matching
论文作者
论文摘要
最近，已经提出了各种深度功能图，从完全监督到完全无监督，具有一系列损失功能以及不同的正则化项。但是，尚不清楚深度功能地图管道的最低成分是什么，以及此类成分是否统一或概括了所有最近在深度功能图上的工作。我们显示了以不同的损失功能获得不同损失功能的最小成分，以获得不同的损失功能，并受到监督和无监督。此外，我们提出了一个新颖的框架，专为完全到满的框架而设计，并且部分构成了完全匹配的匹配，从而在几个基准数据集上实现了最先进的状态，甚至超出了完全监督的方法，从而超过了明显的余量。我们的代码可在https://github.com/not-iitian/weakly-superrevisation-functional-map上公开获取
A variety of deep functional maps have been proposed recently, from fully supervised to totally unsupervised, with a range of loss functions as well as different regularization terms. However, it is still not clear what are minimum ingredients of a deep functional map pipeline and whether such ingredients unify or generalize all recent work on deep functional maps. We show empirically minimum components for obtaining state of the art results with different loss functions, supervised as well as unsupervised. Furthermore, we propose a novel framework designed for both full-to-full as well as partial to full shape matching that achieves state of the art results on several benchmark datasets outperforming even the fully supervised methods by a significant margin. Our code is publicly available at https://github.com/Not-IITian/Weakly-supervised-Functional-map
