Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet

2025-09-05

AI Summary

ModelNet40 suffers from label inconsistency, contamination with 2D samples, scale distortion, and weak inter-class discriminability. To address these issues, this work introduces ModelNet-R—a rigorously curated, high-fidelity benchmark dataset for 3D point cloud classification—and proposes Point-SkipNet, a lightweight graph neural network. Point-SkipNet integrates efficient far-and-near neighbor sampling, dynamic neighborhood grouping, and cross-layer skip connections to achieve structural compactness without sacrificing representational power. It reduces parameter count by over 50% compared to state-of-the-art models while enhancing classification accuracy. Extensive experiments demonstrate that Point-SkipNet achieves a new state-of-the-art accuracy of 94.2% on ModelNet-R. This result empirically validates the critical role of synergistic optimization—jointly improving data quality and model efficiency—in advancing 3D point cloud classification performance.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: 3D Computer Vision
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The classification of 3D point clouds is crucial for applications such as autonomous driving, robotics, and augmented reality. However, the commonly used ModelNet40 dataset suffers from limitations such as inconsistent labeling, 2D data, size mismatches, and inadequate class differentiation, which hinder model performance. This paper introduces ModelNet-R, a meticulously refined version of ModelNet40 designed to address these issues and serve as a more reliable benchmark. Additionally, this paper proposes Point-SkipNet, a lightweight graph-based neural network that leverages efficient sampling, neighborhood grouping, and skip connections to achieve high classification accuracy with reduced computational overhead. Extensive experiments demonstrate that models trained in ModelNet-R exhibit significant performance improvements. Notably, Point-SkipNet achieves state-of-the-art accuracy on ModelNet-R with a substantially lower parameter count compared to contemporary models. This research highlights the crucial role of dataset quality in optimizing model efficiency for 3D point cloud classification. For more details, see the code at: https://github.com/m-saeid/ModeNetR_PointSkipNet.

Problem

• Addressing ModelNet40 dataset limitations for 3D classification
• Proposing lightweight Point-SkipNet for efficient point cloud processing
• Enhancing classification accuracy with refined ModelNet-R benchmark

Innovation

• ModelNet-R refined dataset for reliable benchmarking
• Point-SkipNet lightweight graph neural network architecture
• Skip connections with efficient sampling techniques