Nowadays, point clouds (PCs) are a promising representation format for immersive content and target several emerging applications, notably in virtual and augmented reality. However, efficient coding solutions are critically needed due to the large amount of PC data required for high quality user experiences. To address these needs, several PC coding standards were developed and thus, objective PC quality metrics able to accurately account for the subjective impact of coding artifacts are needed. In this paper, a scale-invariant PC geometry quality assessment metric is proposed based on a new type of correspondence, namely between a point and a distribution of points. This metric is able to reliably measure the geometry quality for PCs with different intrinsic characteristics and degraded by several coding solutions. Experimental results show the superiority of the proposed PC quality metric over relevant state-of-the-art.

中文翻译：

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基于 Mahalanobis 的点到分布度量，用于点云几何质量评估

如今，点云 (PC) 是沉浸式内容的一种很有前途的表示格式，并针对多个新兴应用程序，尤其是在虚拟现实和增强现实中。然而，由于高质量用户体验需要大量 PC 数据，因此迫切需要高效的编码解决方案。为了满足这些需求，开发了多种 PC 编码标准，因此需要能够准确说明编码伪影的主观影响的客观 PC 质量指标。在本文中，基于一种新型的对应关系，即点和点的分布之间，提出了一种尺度不变的 PC 几何质量评估度量。该指标能够可靠地测量具有不同内在特性并被多种编码解决方案降级的 PC 的几何质量。