{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T17:09:57Z","timestamp":1762189797185,"version":"build-2065373602"},"reference-count":84,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Point cloud quality assessment remains a critical challenge due to the high dimensionality and irregular structure of 3D data, as well as the need to align objective predictions with human perception. To solve this, we suggest a novel graph-based learning architecture that integrates perceptual features with advanced graph neural networks. Our method consists of four main stages: First, key perceptual features, including curvature, saliency, and color, are extracted to capture relevant geometric and visual distortions. Second, a graph-based representation of the point cloud is created using these characteristics, where nodes represent perceptual clusters and weighted edges encode their feature similarities, yielding a structured adjacency matrix. Third, a novel Graph Attention Network Transformer Fusion (GATF) module dynamically refines the importance of these features and generates a unified, view-specific representation. Finally, a Graph Convolutional Network (GCN) regresses the fused features into a final quality score. We validate our approach on three benchmark datasets: ICIP2020, WPC, and SJTU-PCQA. Experimental results demonstrate that our method achieves high correlation with human subjective scores, outperforming existing state-of-the-art metrics by effectively modeling the perceptual mechanisms of quality judgment.<\/jats:p>","DOI":"10.3390\/jimaging11110387","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T16:18:42Z","timestamp":1762186722000},"page":"387","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GATF-PCQA: A Graph Attention Transformer Fusion Network for Point Cloud Quality Assessment"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1157-5717","authenticated-orcid":false,"given":"Abdelouahed","family":"Laazoufi","sequence":"first","affiliation":[{"name":"Research Laboratory in Computer Science and Telecommunications (LRIT), Faculty of Sciences, Mohammed V University in Rabat, Rabat 1014, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6741-4799","authenticated-orcid":false,"given":"Mohammed","family":"El Hassouni","sequence":"additional","affiliation":[{"name":"Faculty of Letters and Human Sciences in Rabat, Mohammed V University in Rabat, Rabat 8007, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9124-4921","authenticated-orcid":false,"given":"Hocine","family":"Cherifi","sequence":"additional","affiliation":[{"name":"Carnot Interdisciplinary Laboratory of Burgundy (ICB) UMR 6303 CNRS, University of Burgundy, 21000 Dijon, France"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"ref_1","unstructured":"Mohammadi, P., Ebrahimi-Moghadam, A., and Shirani, S. 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