【摘要】 提出了一种新颖的广义无监督函数映射学习的三维形状密集对应方法。首先，基于多层感知器（multilayer perceptron,MLP）和残差网络，直接学习深度点特征。其次，计算点云的近似测地线距离，并对其进行特征分解，建立特征嵌入空间，引入注意力机制，有效学习广义基函数表示。再次，结合点特征与广义基函数生成三维形状的深度特征表示。最后，建立无监督的函数映射网络框架，获取形状之间的密集对应表示。提出的三元正则优化机制，联合重构损失、特征损失和形状匹配的距离损失，在特征域和空间域上有效提升了学习性能及形状对应的精度。实验结果表明，广义基函数表示与无监督函数映射学习机制适用于任意三维形状，突破了现有方法只适用于连续二维流形的局限，在任意三维形状匹配中取得了更优的性能。更多还原

【Abstract】 This paper proposes a novel dense correspondence method based on generalized unsupervised learning. First,multilayer perceptron(MLP) and residual network are constructed to learn deep point features. Secondly, the approximate geodesic distance of the point cloud is calculated and a feature embedding space is established through feature decomposition. By employing the attention mechanism, it effectively learns the generalized basis function representation. Furthermore, the proposed method combines point features with generalized basis function to generate deep feature representations of 3D shapes. Finally, an unsupervised function mapping network is constructed to obtain dense corresponding representations between shapes. We also propose a tri-regularization mechanism that combines reconstruction loss, descriptor loss, and distance loss for shape matching, effectively improving learning performance and shape corresponding accuracy from the feature and spatial domains. Extensive experimental results have shown that the generalized basis function representation and unsupervised functional map learning mechanism are suitable for arbitrary 3D shapes, breaking through the limitations of previous methods on continuous 2D manifolds, it achieves better performance in arbitrary 3D shape matching.更多还原