Point-LN: A Lightweight Framework for Efficient Point Cloud Classification Using Non-Parametric Positional Encoding
This work addresses the problem of efficient point cloud classification for real-time and resource-constrained applications in computer vision, though it appears incremental as it builds on existing non-parametric methods.
The paper tackles efficient 3D point cloud classification by introducing Point-LN, a lightweight framework that integrates non-parametric components like FPS and k-NN with a learnable classifier, achieving competitive performance on benchmarks such as ModelNet40 and ScanObjectNN while maintaining minimal parameters and low computational costs.
We introduce Point-LN, a novel lightweight framework engineered for efficient 3D point cloud classification. Point-LN integrates essential non-parametric components-such as Farthest Point Sampling (FPS), k-Nearest Neighbors (k-NN), and non-learnable positional encoding-with a streamlined learnable classifier that significantly enhances classification accuracy while maintaining a minimal parameter footprint. This hybrid architecture ensures low computational costs and rapid inference speeds, making Point-LN ideal for real-time and resource-constrained applications. Comprehensive evaluations on benchmark datasets, including ModelNet40 and ScanObjectNN, demonstrate that Point-LN achieves competitive performance compared to state-of-the-art methods, all while offering exceptional efficiency. These results establish Point-LN as a robust and scalable solution for diverse point cloud classification tasks, highlighting its potential for widespread adoption in various computer vision applications.