  Remarkable performance from Transformer networks in Natural Language
Processing promote the development of these models in dealing with computer
vision tasks such as image recognition and segmentation. In this paper, we
introduce a novel framework, called Multi-level Multi-scale Point Transformer
(MLMSPT) that works directly on the irregular point clouds for representation
learning. Specifically, a point pyramid transformer is investigated to model
features with diverse resolutions or scales we defined, followed by a
multi-level transformer module to aggregate contextual information from
different levels of each scale and enhance their interactions. While a
multi-scale transformer module is designed to capture the dependencies among
representations across different scales. Extensive evaluation on public
benchmark datasets demonstrate the effectiveness and the competitive
performance of our methods on 3D shape classification, part segmentation and
semantic segmentation tasks.
