APSIPA Transactions on Signal and Information Processing > Vol 11 > Issue 1

Visual Saliency and Quality Evaluation for 3D Point Clouds and Meshes: An Overview

Weisi Lin, School of Computer Science and Engineering, Nanyang Technological University, Singapore, wslin@ntu.edu.sg , Sanghoon Lee, Electrical and Electronic Engineering, Yonsei University, Korea
Suggested Citation
Weisi Lin and Sanghoon Lee (2022), "Visual Saliency and Quality Evaluation for 3D Point Clouds and Meshes: An Overview", APSIPA Transactions on Signal and Information Processing: Vol. 11: No. 1, e28. http://dx.doi.org/10.1561/116.00000125

Publication Date: 21 Sep 2022
© 2022 W. Lin and S. Lee
Point cloudsmeshes3D visual datasaliencyqualityhuman usesmachine useskeypointshandcrafted featureslearning-based modelingutility-oriented evaluationquality of experience (QoE)metaverse


Open Access

This is published under the terms of CC BY-NC.

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In this article:
Data Acquisition and Representation 
Saliency Modeling 
Quality Evaluation 
Possibilities Ahead 
Summary and Concluding Remarks 


Three-dimensional (3D) point clouds (PCs) and meshes have increasingly become available and indispensable for diversified applications in work and life. In addition, 3D visual data contain information from any viewpoint when needed, introducing new challenges and opportunities. As in the cases of 2D images and videos, computationally modeling saliency and quality for 3D PCs and meshes are important for widespread, economical adaption and optimization. This paper aims to provide a comprehensive overview of the related signal presentation and existing saliency and quality models, with major perspectives from the ultimate users (i.e., humans or machines), modeling methodology (with handcrafted features or machine learning), and modeling scope (generic or utility-oriented models). Possible future research directions are also discussed.