Point-LN: A Lightweight Framework for Efficient Point Cloud Classification Using Non-Parametric Positional Encoding

TL;DR

Point-LN is a lightweight, efficient point cloud classification framework that combines non-parametric components with a simple learnable classifier, achieving high accuracy with minimal computational resources.

Contribution

It introduces a hybrid architecture integrating non-parametric methods with a streamlined classifier, enhancing efficiency and accuracy in point cloud classification.

Findings

Achieves competitive accuracy on ModelNet40 and ScanObjectNN datasets.

Maintains low computational costs and rapid inference speeds.

Offers a scalable solution suitable for resource-constrained environments.

Abstract

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.