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Found 3 papers, 0 papers with code
On the Fourier analysis in the SO(3) space : EquiLoPO Network
This work suggests the benefits of true rotational equivariance on SO(3) and flexible unconstrained filters enabled by the local activation function, providing a flexible framework for equivariant deep learning on volumetric data with potential applications across domains.
ILPO-NET: Network for the invariant recognition of arbitrary volumetric patterns in 3D
Effective recognition of spatial patterns and learning their hierarchy is crucial in modern spatial data analysis.
6DCNN with roto-translational convolution filters for volumetric data processing
In this work, we introduce 6D Convolutional Neural Network (6DCNN) designed to tackle the problem of detecting relative positions and orientations of local patterns when processing three-dimensional volumetric data.
