  In this paper, we present Position-to-Structure Attention Transformers
(PS-Former), a Transformer-based algorithm for 3D point cloud recognition.
PS-Former deals with the challenge in 3D point cloud representation where
points are not positioned in a fixed grid structure and have limited feature
description (only 3D coordinates ($x, y, z$) for scattered points). Existing
Transformer-based architectures in this domain often require a pre-specified
feature engineering step to extract point features. Here, we introduce two new
aspects in PS-Former: 1) a learnable condensation layer that performs point
downsampling and feature extraction; and 2) a Position-to-Structure Attention
mechanism that recursively enriches the structural information with the
position attention branch. Compared with the competing methods, while being
generic with less heuristics feature designs, PS-Former demonstrates
competitive experimental results on three 3D point cloud tasks including
classification, part segmentation, and scene segmentation.
