Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPR42600.2020.01124 | CVPR |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
29 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher Bongsoo Choy | 1 | 137 | 6.00 |
| Lee Junha | 2 | 0 | 0.34 |
| Rene Ranftl | 3 | 646 | 29.52 |
| Jaesik Park | 4 | 496 | 24.88 |
| Vladlen Koltun | 5 | 4064 | 162.63 |