2021/11/30 by Georg Bökman, Fredrik Kahl, Bökman, Georg +3
Computer Science · Engineering · #Advanced Vision and Imaging #Robotics and Sensor-Based Localization #Optical measurement and interference techniques
paper · pdf · doi:10.48550/arxiv.2111.15341
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.