论文标题
用于几何模式识别的高维卷积网络
High-dimensional Convolutional Networks for Geometric Pattern Recognition
论文作者
论文摘要
科学和工程中的许多问题可以根据高维空间的几何模式提出。我们提出了高维卷积网络（Convnets），以解决几何注册背景下出现的模式识别问题。我们首先研究了卷积网络在检测高维空间中线性子空间中的有效性：比Convnets的先前应用高得多的维度：维度要高得多。然后，我们将高维交响器应用于刚体动作和图像对应关系估计下的3D注册。实验表明，我们的高维转向网络优于基于全球合并运营商的深网的先验方法。
Many problems in science and engineering can be formulated in terms of geometric patterns in high-dimensional spaces. We present high-dimensional convolutional networks (ConvNets) for pattern recognition problems that arise in the context of geometric registration. We first study the effectiveness of convolutional networks in detecting linear subspaces in high-dimensional spaces with up to 32 dimensions: much higher dimensionality than prior applications of ConvNets. We then apply high-dimensional ConvNets to 3D registration under rigid motions and image correspondence estimation. Experiments indicate that our high-dimensional ConvNets outperform prior approaches that relied on deep networks based on global pooling operators.
