Equivariance to permutations and rigid motions is an important inductive bias for various 3D learning problems. Recently it has been shown that the equivariant Tensor Field Network architecture is universal -- it can approximate any equivariant function. In this paper we suggest a much simpler architecture, prove that it enjoys the same universality guarantees and evaluate its performance on Modelnet40. The code to reproduce our experiments is available at \url{https://github.com/simpleinvariance/UniversalNetwork}

翻译：对各种3D学习问题来说,变异和僵硬动议的等同性是一个重要的感性偏向。最近已经表明,等异性租户外地网络结构是普遍的 -- -- 它可以大致地概括任何等异性功能。在本文中,我们建议一个简单得多的结构,证明它享有同样的普遍性保障,并评价其在Modelnet40上的表现。复制我们的实验的代码可以在以下网址查阅:https://github.com/solunistancy/UniversalNetwork}