An Efficient and Lightweight Point Cloud Recognition Network Based on Neighborhood Learning

Point cloud recognition has wide applications in fields such as autonomous driving and shape classification. Although significant progress has been made in point cloud processing in recent years, most of it has been achieved by designing more complex networks to attain better performance. This paper proposes a novel lightweight point cloud recognition network by introducing a new local neighborhood optimization layer (LNOL), which improves traditional sampling methods by correlation learning in local area. The LNOL is embedded within a single-layer local transformer architecture, significantly reducing computational complexity and parameters while maintaining the model's expressive power. Experimental results on the ModelNet40 benchmark dataset demonstrate that our method achieves a classification accuracy of 93.3% and an average precision of 92.0% without using a voting strategy. Compared to the mainstream local transformer model point transformer, our network requires only 9.95G FLOPs and 2.33M parameters, reducing computational cost by 94.7% and parameter count by 75.7%, with only a 0.4% drop in accuracy. This study provides an efficient solution for real-time 3D recognition applications, significantly lowering computational resource requirements while maintaining performance.