Unified Approach to Mesh Saliency : Evaluating Textured and Non-Textured Meshes Through VR and Multifunctional Prediction

Mesh saliency aims to empower artificial intelligence with strong adaptability to highlight regions that naturally attract visual attention. Existing advances primarily emphasize the crucial role of geometric shapes in determining mesh saliency, but it remains challenging to flexibly sense the uniqu...

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Publié dans:IEEE transactions on visualization and computer graphics. - 1996. - 31(2025), 5 vom: 21. Mai, Seite 3151-3160
Auteur principal: Zhang, Kaiwei (Auteur)
Autres auteurs: Zhu, Dandan, Min, Xiongkuo, Zhai, Guangtao
Format: Article en ligne
Langue:English
Publié: 2025
Accès à la collection:IEEE transactions on visualization and computer graphics
Sujets:Journal Article
Description
Résumé:Mesh saliency aims to empower artificial intelligence with strong adaptability to highlight regions that naturally attract visual attention. Existing advances primarily emphasize the crucial role of geometric shapes in determining mesh saliency, but it remains challenging to flexibly sense the unique visual appeal brought by the realism of complex texture patterns. To investigate the interaction between geometric shapes and texture features in visual perception, we establish a comprehensive mesh saliency dataset, capturing saliency distributions for identical 3D models under both non-textured and textured conditions. Additionally, we propose a unified saliency prediction model applicable to various mesh types, providing valuable insights for both detailed modeling and realistic rendering applications. This model effectively analyzes the geometric structure of the mesh while seamlessly incorporating texture features into the topological framework, ensuring coherence throughout appearance-enhanced modeling. Through extensive theoretical and empirical validation, our approach not only enhances performance across different mesh types, but also demonstrates the model's scalability and generalizability, particularly through cross-validation of various visual features
Description:Date Revised 28.04.2025
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1941-0506
DOI:10.1109/TVCG.2025.3549550