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

• URL: http://arxiv.org/abs/2111.15341v1
• Date: Tue, 30 Nov 2021 12:37:36 GMT
• Title: ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds
• Authors: Georg B\"okman, Fredrik Kahl and Axel Flinth
• Abstract summary: We propose a novel neural network architecture for processing 2D point clouds. We show how to extend the architecture to accept a set of 2D-2D correspondences as indata. Experiments are presented on the estimation of essential matrices in stereo vision.
• Score: 17.35440223078089
• License: http://creativecommons.org/licenses/by/4.0/
• 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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