PatchMixer: Rethinking network design to boost generalization for 3D
  point cloud understanding
Davide Boscaini Fabio Poiesi
The recent trend in deep learning methods for 3D point cloud understanding is
to propose increasingly sophisticated architectures either to better capture 3D
geometries or by introducing possibly undesired inductive biases. Moreover,
prior works introducing novel architectures compared their performance on the
same domain, devoting less attention to their generalization to other domains.
We argue that the ability of a model to transfer the learnt knowledge to
different domains is an important feature that should be evaluated to
exhaustively assess the quality of a deep network architecture. In this work we
propose PatchMixer, a simple yet effective architecture that extends the ideas
behind the recent MLP-Mixer paper to 3D point clouds. The novelties of our
approach are the processing of local patches instead of the whole shape to
promote robustness to partial point clouds, and the aggregation of patch-wise
features using an MLP as a simpler alternative to the graph convolutions or the
attention mechanisms that are used in prior works. We evaluated our method on
the shape classification and part segmentation tasks, achieving superior
generalization performance compared to a selection of the most relevant deep
architectures.