GitHub10d
Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis
To this end, we propose 3D Graph Convolution Networks (3D-GCN), which is designed to extract local 3D features from point clouds across scales, while shift and scale-invariance properties are ...
GitHub27d
Dyanmic Adaptive Spatio-temporal GCN for fMRI modelling (DAST-GCN)
This is the original pytorch implementation of DAST-GCN in the paper Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling ...