Although remarkable advancements have been made recently in point cloud
analysis through the exploration of transformer architecture, it remains
challenging to effectively learn local and global structures within point
clouds. In this paper, we propose a new transformer architecture equipped with
a collect-and-distribute mechanism to communicate short- and long-range
contexts of point clouds, which we refer to as CDFormer. Specifically, we first
utilize self-attention to capture short-range interactions within each local
patch, and the updated local features are then collected into a set of proxy
reference points from which we can extract long-range contexts. Afterward, we
distribute the learned long-range contexts back to local points via
cross-attention. To address the position clues for short- and long-range
contexts, we also introduce context-aware position encoding to facilitate
position-aware communications between points. We perform experiments on four
popular point cloud datasets, namely ModelNet40, ScanObjectNN, S3DIS, and
ShapeNetPart, for classification and segmentation. Results show the
effectiveness of the proposed CDFormer, delivering several new state-of-the-art
performances on point cloud classification and segmentation tasks. The code is
available at https://github.com/haibo-qiu/CDFormer.

本研究提出了 CDFormer，一种新的利用收集和分布机制的 Transformer 架构，可对点云的局部和全局结构进行有效学习，并在四个流行的点云数据集上取得了新的最佳分类和分割结果。