  In this work, we present Point Transformer, a deep neural network that
operates directly on unordered and unstructured point sets. We design Point
Transformer to extract local and global features and relate both
representations by introducing the local-global attention mechanism, which aims
to capture spatial point relations and shape information. For that purpose, we
propose SortNet, as part of the Point Transformer, which induces input
permutation invariance by selecting points based on a learned score. The output
of Point Transformer is a sorted and permutation invariant feature list that
can directly be incorporated into common computer vision applications. We
evaluate our approach on standard classification and part segmentation
benchmarks to demonstrate competitive results compared to the prior work. Code
is publicly available at: https://github.com/engelnico/point-transformer
