Abstract
This paper presents a summary of recent progress in compression, subjective assessment and objective quality measures of point cloud representations of three dimensional visual information. Different existing point cloud datasets, as well as discusses the protocols that have been proposed to evaluate the subjective quality of point cloud data. Several geometry and attribute point cloud data objective quality measures are also presented and described. A case study on the evaluation of subjective quality of point clouds in two laboratories is presented. Six original point clouds degraded with G-PCC and V-PCC point cloud compression and five degradation levels were subjectively evaluated, showing high inter-laboratory correlation. Furthermore, performance of several geometry-based objective quality measures applied to the same data are described, concluding that the highest correlation with subjective scores is obtained using point-to-plane measures. Finally, several current challenges and future research directions on point clouds compression and quality evaluation are discussed.
Highlights
A point cloud is a set of discrete data points defined in a given coordinate space—for example displays or by displaying approximating surfaces after applying a suitable reconstruction algorithm [1].An example point cloud, “dragon”, from [2], is shown in Figure 1, where the left image shows the point cloud points rendered directly and viewed from a specific observation point and the right image shows the same point cloud after surface reconstruction using volumetric merging [3].Point clouds can have from a few hundred thousand to several million points and require tens of megabytes for storing the set of point coordinates and point attributes such as color and normal vector information
We present a general framework for subjective evaluation of point clouds, as well as currently proposed objective metrics for point cloud quality measurement
Afterwards, we present a case study using results from subjective evaluations of point clouds performed in a collaboration between two international laboratories at the University of Coimbra in Portugal and the University North in Croatia
Summary
A point cloud is a set of discrete data points defined in a given coordinate space—for example. This paper presents an overview of existing methods for point cloud compression and the research problems involved in evaluating the perceived (subjective) visual quality of point clouds and estimating that quality using computable models, using some of the activities of the JPEG PC AhG as a case study. A list of some recent point cloud test datasets is presented, and the protocols that have been proposed to evaluate the subjective quality of point clouds are described.
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