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Convolutional Neural Networks (CNNs) traditionally encode translationequivariance via the convolution operation. Generalization to othertransformations has recently received attraction to encode the knowledge of thedata geometry in group convolution operations. Equivariance to rotation isparticularly important for 3D image analysis due to the large diversity ofpossible pattern orientations. 3D texture is a particularly important cue forthe analysis of medical images such as CT and MRI scans as it describesdifferent types of tissues and lesions. In this paper, we evaluate the use of3D group equivariant CNNs accounting for the simplified group of right-anglerotations to classify 3D synthetic textures from a publicly available dataset.The results validate the importance of rotation equivariance in a controlledsetup and yet motivate the use of a finer coverage of orientations in order toobtain equivariance to realistic rotations present in 3D textures.

Rotational 3D Texture Classification Using Group Equivariant CNNs