mlsanityData to 14 Oct 2026

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

arXiv 2506.04081 ↗First posted 4 Jun 2025cs.CVcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 90 references that are in the map. First posted on arXiv 4 Jun 2025.

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25 of 90 references.

References · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
  4. 2021Activation Functions in Deep Learning: A Comprehensive Survey and Benchmarkcited by 155
  5. 2021Adaptive Graph Convolution for Point Cloud Analysiscited by 60
  6. 2020Inferring Point Cloud Quality via Graph Similaritycited by 58
  7. 2021No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Modelscited by 50
  8. 2021Perceptual Quality Assessment of Colored 3D Point Cloudscited by 49
  9. 20183D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Modelcited by 47
  10. 2022MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessmentcited by 39
  11. 2021No-Reference Point Cloud Quality Assessment via Domain Adaptationcited by 38
  12. 2020Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compressioncited by 34
13 more
  1. 2021MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancycited by 28
  2. 2022A No-reference Quality Assessment Metric for Point Cloud Based on Captured Video Sequencescited by 24
  3. 2022GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Networkcited by 22
  4. 2023GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessmentcited by 21
  5. 2020A generalized Hausdorff distance based quality metric for point cloud geometrycited by 15
  6. 2014Subjective and Objective Quality Assessment of Image: A Surveycited by 14
  7. 2024Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessmentcited by 14
  8. 2018Eigendecomposition-Free Sampling Set Selection for Graph Signalscited by 9
  9. 2023Reduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projectioncited by 9
  10. 2022PCQA-GRAPHPOINT: Efficients Deep-Based Graph Metric For Point Cloud Quality Assessmentcited by 8
  11. 2024PAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessmentcited by 4
  12. 2019A General Framework for Complex Network-Based Image Segmentationcited by 3
  13. 2024Learning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimizationcited by 2

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