Point Cloud Quality Assessment Using the Perceptual Clustering Weighted Graph (PCW-Graph) and Attention Fusion Network

2025-06-04

AI Summary

To address the challenge of quantifying 3D point cloud distortion in real-world scenarios—where pristine reference models are unavailable and distortion is difficult to measure—this paper proposes a novel no-reference point cloud quality assessment (NR-PCQA) method. The core innovation lies in constructing a perceptual clustering-weighted graph (PCW-Graph) that explicitly models local geometric and semantic inconsistencies, coupled with an attention-based fusion network for adaptive, multi-scale feature aggregation. The method integrates graph neural networks, self-attention mechanisms, and perception-driven graph construction. Evaluated on multiple standard benchmarks, it achieves state-of-the-art performance, demonstrating significantly improved discrimination capability against geometric noise, compression artifacts, and sampling distortions. Specifically, it attains an average improvement of 12.7% in Pearson linear correlation coefficient (PLCC) over prior approaches.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

No-Reference Point Cloud Quality Assessment (NR-PCQA) is critical for evaluating 3D content in real-world applications where reference models are unavailable.

Problem

• NR-PCQA evaluates 3D content without reference models
• PCW-Graph and Attention Fusion Network assess point cloud quality
• Perceptual clustering improves quality assessment accuracy

Innovation

• Perceptual Clustering Weighted Graph for quality assessment
• Attention Fusion Network enhances evaluation accuracy
• No-Reference Point Cloud Quality Assessment method