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arXiv · 2111.15341

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

Georg Bökman·Fredrik Kahl·Axel Flinth
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Abstract

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

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Georg Bökman, Fredrik Kahl, Axel Flinth. 2022-03-28. ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds. https://arxiv.org/abs/2111.15341

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