Spherical CNNs


Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling.

This paper introduces the building blocks for constructing spherical CNNs proposing a definition for the spherical cross-correlation that is both expressive and rotation-equivariant.  


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