Papers › High-dimensional Convolutional Networks for Geometric Pattern Recognition
High-dimensional Convolutional Networks for Geometric Pattern Recognition
Christopher Choy, Junha Lee, Rene Ranftl, Jaesik Park, Vladlen Koltun
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.
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