TY - JOUR
T1 - Textured Mesh Saliency
T2 - 39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025
AU - Zhang, Kaiwei
AU - Zhu, Dandan
AU - Min, Xiongkuo
AU - Zhai, Guangtao
N1 - Publisher Copyright:
Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2025/4/11
Y1 - 2025/4/11
N2 - Textured meshes significantly enhance the realism and detail of objects by mapping intricate texture details onto the geometric structure of 3D models. This advancement is valuable across various applications, including entertainment, education, and industry. While traditional mesh saliency studies focus on non-textured meshes, our work explores the complexities introduced by detailed texture patterns. We present a new dataset for textured mesh saliency, created through an innovative eye-tracking experiment in a six degrees of freedom (6-DOF) VR environment. This dataset addresses the limitations of previous studies by providing comprehensive eye-tracking data from multiple viewpoints, thereby advancing our understanding of human visual behavior and supporting more accurate and effective 3D content creation. Our proposed model predicts saliency maps for textured mesh surfaces by treating each triangular face as an individual unit and assigning a saliency density value to reflect the importance of each local surface region. The model incorporates a texture alignment module and a geometric extraction module, combined with an aggregation module to integrate texture and geometry for precise saliency prediction. We believe this approach will enhance the visual fidelity of geometric processing while ensuring computational efficiency, essential for real-time rendering and high-detail applications such as VR and gaming.
AB - Textured meshes significantly enhance the realism and detail of objects by mapping intricate texture details onto the geometric structure of 3D models. This advancement is valuable across various applications, including entertainment, education, and industry. While traditional mesh saliency studies focus on non-textured meshes, our work explores the complexities introduced by detailed texture patterns. We present a new dataset for textured mesh saliency, created through an innovative eye-tracking experiment in a six degrees of freedom (6-DOF) VR environment. This dataset addresses the limitations of previous studies by providing comprehensive eye-tracking data from multiple viewpoints, thereby advancing our understanding of human visual behavior and supporting more accurate and effective 3D content creation. Our proposed model predicts saliency maps for textured mesh surfaces by treating each triangular face as an individual unit and assigning a saliency density value to reflect the importance of each local surface region. The model incorporates a texture alignment module and a geometric extraction module, combined with an aggregation module to integrate texture and geometry for precise saliency prediction. We believe this approach will enhance the visual fidelity of geometric processing while ensuring computational efficiency, essential for real-time rendering and high-detail applications such as VR and gaming.
UR - https://www.scopus.com/pages/publications/105003906174
U2 - 10.1609/aaai.v39i9.33082
DO - 10.1609/aaai.v39i9.33082
M3 - 会议文章
AN - SCOPUS:105003906174
SN - 2159-5399
VL - 39
SP - 9977
EP - 9984
JO - Proceedings of the AAAI Conference on Artificial Intelligence
JF - Proceedings of the AAAI Conference on Artificial Intelligence
IS - 9
Y2 - 25 February 2025 through 4 March 2025
ER -