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.

翻译：点云(PCs)是许多新兴应用领域,特别是虚拟和扩展现实和自主工具的强有力的三维视觉代表模式,然而,高度沉浸和现实经验所需的大量个人计算机数据需要高效、丢失的PC编码解决方案,至关重要;最近,已经制定了两个MPEG 个人计算机编码标准,以满足相关应用要求,并预期今后会进一步发展;在这方面,评估个人计算机质量,特别是已解码的个人计算机的质量至关重要,需要设计高效客观的PC质量衡量标准;在本文件中,提出了用于个人计算机质量评估的新的点到分布衡量标准,同时考虑几何和纹理;这一新的质量衡量标准利用了马哈拉诺比距离的规模变化属性,首先评估几何和颜色点到分布的扭曲,而后者是在为获得联合几何测量和颜色质量衡量标准而结合后,因此,拟议的质量衡量标准大大超出文献中的最佳个人计算机质量评估指标。