Rotation-equivariant spherical vector networks for objects recognition with unknown poses

Analyzing 3D objects without pose priors using neural networks is challenging. In view of the shortcoming that spherical convolutional networks lack the construction of a part-whole hierarchy with rotation equivariance for 3D object recognition with unknown poses, which generates whole rotation-equivariant features that cannot be effectively preserved, a rotation-equivariant part-whole hierarchy spherical vector network is proposed in this paper. In our experiments, we map a 3D object onto the unit sphere, construct an ordered list of vectors from the convolutional layers of the rotation-equivariant spherical convolutional network and then construct a part-whole hierarchy to classify the 3D object using the proposed rotation-equivariant routing algorithm. The experimental results show that the proposed method improves not only the recognition of 3D objects with known poses, but also the recognition of 3D objects with unknown poses compared to previous spherical convolutional neural networks. This finding validates the construction of the rotation-equivariant part-whole hierarchy.