A Point-to-Distribution Joint Geometry and Color Metric for Point Cloud Quality Assessment
This work addresses the need for efficient objective quality metrics for point clouds in emerging domains, representing an incremental improvement over prior methods.
The paper tackles the problem of assessing the quality of decoded point clouds, which is critical for applications like virtual reality and autonomous vehicles, by proposing a novel point-to-distribution metric that considers both geometry and texture, and it significantly outperforms existing state-of-the-art metrics.
Point clouds (PCs) are a powerful 3D visual representation paradigm for many emerging application domains, especially virtual and augmented reality, and autonomous vehicles. However, the large amount of PC data required for highly immersive and realistic experiences requires the availability of efficient, lossy PC coding solutions are critical. Recently, two MPEG PC coding standards have been developed to address the relevant application requirements and further developments are expected in the future. In this context, the assessment of PC quality, notably for decoded PCs, is critical and asks for the design of efficient objective PC quality metrics. In this paper, a novel point-to-distribution metric is proposed for PC quality assessment considering both the geometry and texture. This new quality metric exploits the scale-invariance property of the Mahalanobis distance to assess first the geometry and color point-to-distribution distortions, which are after fused to obtain a joint geometry and color quality metric. The proposed quality metric significantly outperforms the best PC quality assessment metrics in the literature.