Unlocking Hidden Potential in Point Cloud Networks with Attention-Guided Grouping-Feature Coordination

2025-09-20

AI Summary

To address insufficient coordination between grouping and feature extraction layers in point cloud networks—which hinders full exploitation of raw point potentials—this paper proposes a module-level optimization approach. We design a lightweight Grouping-Feature Coordination (GF-Core) module, the first to jointly and dynamically regulate both grouping and feature extraction layers. To enhance geometric fidelity and discriminability, we introduce an attention-guided separable mechanism and a coordinate-feature joint similarity-based grouping strategy. Furthermore, we develop a self-supervised contrastive pretraining framework tailored for point clouds to improve robustness. Our method is architecture-agnostic and achieves 94.0% accuracy on ModelNet40—comparable to state-of-the-art methods—while outperforming baselines by 2.96%, 6.34%, and 6.32% on the three ScanObjectNN variants, respectively. These results demonstrate significant gains in generalization and robustness under real-world scanning conditions.

Technology Category

Search and Optimization: Learning to SearchComputer Vision: Learning & Optimization for CVMachine Learning: Learning with Manifolds

Application Category

Web Mining and Content Analysis: Large pretrained models with web dataGraph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Enhancing underutilized potential in point cloud networks
• Coordinating grouping and feature extraction layers strategically
• Improving feature aggregation for complex point cloud analysis

Innovation

• Lightweight module coordinates grouping and feature extraction layers
• Self-supervised pretraining strategy enhances point cloud robustness
• Strategic module integration boosts performance without structural modifications