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In this paper, we are concerned with rotation equivariance on 2D point clouddata. We describe a particular set of functions able to approximate anycontinuous rotation equivariant and permutation invariant function. Based onthis result, we propose a novel neural network architecture for processing 2Dpoint clouds and we prove its universality for approximating functionsexhibiting these symmetries.  We also show how to extend the architecture to accept a set of 2D-2Dcorrespondences as indata, while maintaining similar equivariance properties.Experiments are presented on the estimation of essential matrices in stereovision.

ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds