Joint Geometry and Color Projection-based Point Cloud Quality Metric
Alireza Javaheri, Catarina Brites, Fernando Pereira, Jo\~ao Ascenso

TL;DR
This paper introduces a novel point cloud quality metric that combines geometry and color projections to improve alignment accuracy, leveraging advanced 2D quality metrics for better correlation with human perception.
Contribution
It proposes a joint geometry and color projection-based metric that addresses misalignment issues and utilizes top-performing 2D metrics for enhanced quality assessment.
Findings
Achieves the best subjective-objective correlation among compared metrics.
Shows 17% and 14.2% Pearson correlation improvements over D1-PSNR and D2-PSNR.
Demonstrates robustness across various coding degradations.
Abstract
Point cloud coding solutions have been recently standardized to address the needs of multiple application scenarios. The design and assessment of point cloud coding methods require reliable objective quality metrics to evaluate the level of degradation introduced by compression or any other type of processing. Several point cloud objective quality metrics has been recently proposed to reliable estimate human perceived quality, including the so-called projection-based metrics. In this context, this paper proposes a joint geometry and color projection-based point cloud objective quality metric which solves the critical weakness of this type of quality metrics, i.e., the misalignment between the reference and degraded projected images. Moreover, the proposed point cloud quality metric exploits the best performing 2D quality metrics in the literature to assess the quality of the projected…
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Taxonomy
Topics3D Shape Modeling and Analysis · Optical measurement and interference techniques · 3D Surveying and Cultural Heritage
