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Blind Quality Assessment of 3D Dense Point Clouds with Structure Guided Resampling by Zhou, Wei; Yang, Qi; Jiang, Qiuping; Zhai, Guangtao; Lin, Weisi is a scholarly article available to read on EtoBox.

What is Blind Quality Assessment of 3D Dense Point Clouds with Structure Guided Resampling about?

Objective quality assessment of 3D point clouds is essential for the development of immersive multimedia systems in real-world applications. Despite the success of perceptual quality evaluation for 2D images and videos, blind/no-reference metrics are still scarce for 3D point clouds with large-scale irregularly distributed 3D points. Therefore, in this paper, we propose an objective point cloud quality index with Structure Guided Resampling (SGR) to automatically evaluate the perceptually visual quality of 3D dense point clouds. The proposed SGR is a general-purpose blind quality assessment method without the assistance of any reference information. Specifically, considering that the human visual system (HVS) is highly sensitive to structure information, we first exploit the unique normal vectors of point clouds to execute regional pre-processing which consists of keypoint resampling and local region construction. Then, we extract three groups of quality-related features, including: 1) geometry density features; 2) color naturalness features; 3) angular consistency features. Both the cognitive peculiarities of the human brain and naturalness regularity are involved in the designed

Author

Zhou, Wei; Yang, Qi; Jiang, Qiuping; Zhai, Guangtao; Lin, Weisi

Published

2022

Language

EN