The usage of 3D vision algorithms, such as shape reconstruction, remains
limited because they require inputs to be at a fixed canonical rotation.
Recently, a simple equivariant network, Vector Neuron (VN) has been proposed
that can be easily used with the state-of-the-art 3D neural network (NN)
architectures. However, its performance is limited because it is designed to
use only three-dimensional features, which is insufficient to capture the
details present in 3D data. In this paper, we introduce an equivariant feature
representation for mapping a 3D point to a high-dimensional feature space. Our
feature can discern multiple frequencies present in 3D data, which is the key
to designing an expressive feature for 3D vision tasks. Our representation can
be used as an input to VNs, and the results demonstrate that with our feature
representation, VN captures more details, overcoming the limitation raised in
its original paper.

介绍了一种用于将 3D 点映射到高维特征空间的等变特征表示方法，该特征能够识别 3D 数据中存在的多个频率，以提供更多细节来克服原始研究中的限制。