  Recently, vision architectures based exclusively on multi-layer perceptrons
(MLPs) have gained much attention in the computer vision community. MLP-like
models achieve competitive performance on a single 2D image classification with
less inductive bias without hand-crafted convolution layers. In this work, we
explore the effectiveness of MLP-based architecture for the view-based 3D
object recognition task. We present an MLP-based architecture termed as
Round-Roll MLP (R$^2$-MLP). It extends the spatial-shift MLP backbone by
considering the communications between patches from different views. R$^2$-MLP
rolls part of the channels along the view dimension and promotes information
exchange between neighboring views. We benchmark MLP results on ModelNet10 and
ModelNet40 datasets with ablations in various aspects. The experimental results
show that, with a conceptually simple structure, our R$^2$-MLP achieves
competitive performance compared with existing state-of-the-art methods.
