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

2025-01-24

AI Summary

To address real-time 3D point cloud classification under resource-constrained settings, this paper proposes Point-LN, a lightweight framework. Methodologically, it introduces, for the first time, a non-parametric (i.e., non-learnable) sinusoidal positional encoding—replacing conventional learnable position embeddings—and integrates it with Farthest Point Sampling, k-NN neighborhood construction, and a minimal fully connected classifier, eliminating complex modules. Its core contribution lies in the synergistic architecture of non-parametric positional encoding and a lightweight learnable classification head, achieving strong representational capacity with drastically reduced model complexity. On ModelNet40 and ScanObjectNN, Point-LN attains state-of-the-art accuracy while reducing parameter count by over 60% and accelerating inference by 2.3×, outperforming existing lightweight models in overall efficiency–accuracy trade-off.

Technology Category

• Machine Learning: Learning with Manifolds
• Search and Optimization: Learning to Search
• Computer Vision: 3D Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• 3D point cloud
• fast processing
• resource efficiency

Innovation

• Parameter-less Position Encoding
• Efficient Point Cloud Classification
• Real-time Processing Capability