论文标题
无监督的比例不变的多光谱形状匹配
Unsupervised Scale-Invariant Multispectral Shape Matching
论文作者
论文摘要
非刚性可拉伸结构之间的对齐是计算机视觉中最具挑战性的任务之一，因为不变的属性很难定义，并且没有针对真实数据集的标记数据。我们根据比例不变的几何形状的光谱域提出了无监督的神经网络体系结构。我们在功能地图体系结构的基础上构建，但是表明，一旦等轴测假设破裂，学习本地特征就不够了。我们证明了使用多个量表不变的几何形状来解决此问题。我们的方法对局部规模的变形不可知，与现有的光谱最新溶液相比，来自不同领域的匹配形状的性能出色。
Alignment between non-rigid stretchable structures is one of the most challenging tasks in computer vision, as the invariant properties are hard to define, and there is no labeled data for real datasets. We present unsupervised neural network architecture based upon the spectral domain of scale-invariant geometry. We build on top of the functional maps architecture, but show that learning local features, as done until now, is not enough once the isometry assumption breaks. We demonstrate the use of multiple scale-invariant geometries for solving this problem. Our method is agnostic to local-scale deformations and shows superior performance for matching shapes from different domains when compared to existing spectral state-of-the-art solutions.
