High-dimensional Convolutional Networks for Geometric Pattern Recognition
Autor: | Choy, Christopher, Lee, Junha, Ranftl, Rene, Park, Jaesik, Koltun, Vladlen |
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Rok vydání: | 2020 |
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Druh dokumentu: | Working Paper |
Popis: | 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. Comment: Accepted for CVPR 2020 oral presentation |
Databáze: | arXiv |
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