Unlocking Hidden Potential in Point Cloud Networks with Attention-Guided Grouping-Feature Coordination
This work addresses performance bottlenecks in point cloud analysis for computer vision applications, offering an incremental improvement through strategic module integration.
The paper tackled the underutilized potential in point cloud networks by proposing a lightweight Grouping-Feature Coordination Module and a self-supervised pretraining strategy, achieving 94.0% accuracy on ModelNet40 and improvements of up to 6.34% on ScanObjectNN variants.
Point cloud analysis has evolved with diverse network architectures, while existing works predominantly focus on introducing novel structural designs. However, conventional point-based architectures - processing raw points through sequential sampling, grouping, and feature extraction layers - demonstrate underutilized potential. We notice that substantial performance gains can be unlocked through strategic module integration rather than structural modifications. In this paper, we propose the Grouping-Feature Coordination Module (GF-Core), a lightweight separable component that simultaneously regulates both grouping layer and feature extraction layer to enable more nuanced feature aggregation. Besides, we introduce a self-supervised pretraining strategy specifically tailored for point-based inputs to enhance model robustness in complex point cloud analysis scenarios. On ModelNet40 dataset, our method elevates baseline networks to 94.0% accuracy, matching advanced frameworks' performance while preserving architectural simplicity. On three variants of the ScanObjectNN dataset, we obtain improvements of 2.96%, 6.34%, and 6.32% respectively.