Collect-and-Distribute Transformer for 3D Point Cloud Analysis
- URL: http://arxiv.org/abs/2306.01257v2
- Date: Tue, 31 Oct 2023 03:55:12 GMT
- Title: Collect-and-Distribute Transformer for 3D Point Cloud Analysis
- Authors: Haibo Qiu, Baosheng Yu, Dacheng Tao
- Abstract summary: We propose a new transformer network equipped with a collect-and-distribute mechanism to communicate short- and long-range contexts of point clouds.
Results show the effectiveness of the proposed CDFormer, delivering several new state-of-the-art performances on point cloud classification and segmentation tasks.
- Score: 82.03517861433849
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Remarkable advancements have been made recently in point cloud analysis
through the exploration of transformer architecture, but it remains challenging
to effectively learn local and global structures within point clouds. In this
paper, we propose a new transformer network 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 employ
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 additionally
introduce the context-aware position encoding to facilitate position-aware
communications between points. We perform experiments on five popular point
cloud datasets, namely ModelNet40, ScanObjectNN, ShapeNetPart, S3DIS and
ScanNetV2, 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 source code is
available at \url{https://github.com/haibo-qiu/CDFormer}.
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