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Found 3 papers, 2 papers with code
SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data
This network can then operate on any data that can be represented as a positional graph.
Interpolated SelectionConv for Spherical Images and Surfaces
Our approach represents the surface as a graph of connected points that doesn't rely on a particular sampling strategy.
Ranked #22 on
Semantic Segmentation
on Stanford2D3D Panoramic
Style Transfer for Light Field Photography
Thus, it is necessary to adapt existing monocular style transfer networks in a way that allows for the stylization of each view of the light field while maintaining visual consistencies between views.
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