Perceptual Quality Assessment of 3d Point Clouds

Abstract

The real-world applications of 3D point clouds have been growing rapidly in recent years, but not much effective work has been dedicated to perceptual quality assessment of colored 3D point clouds. In this work, we first build a large 3D point cloud database for subjective and objective quality assessment of point clouds. We construct 20 high quality, realistic, and omni-directional point clouds of diverse contents. We then apply downsampling, Gaussian noise, and three types of compression algorithms to create 740 distorted point clouds. We carry out a subjective experiment to evaluate the quality of distorted point clouds. Our statistical analysis finds that existing objective point cloud quality assessment (PCQA) models only achieve limited success in predicting subjective quality ratings. We propose a novel objective PCQA model based on the principle of information content weighted structural similarity. Our experimental results show that the proposed model well correlates with subjective opinions and significantly outperforms the existing PCQA models.

PCQM: A Full-Reference Quality Metric for Colored 3D Point Clouds

“…This difference may be due to the fact that we consider only an asymmetric distance (from R to D). However, despite its good results on this dataset, the pure geometric distance has shown to behave very poorly in other datasets (e.g., [24]- [26]).…”

“…one single type of degradation, such as in [23]). However, they report bad results for most of subjective datasets [24]- [26]. Very recently, Alexiou et al [25] proposed a metric based on differences of normal orientations and Meynet et al [6] proposed a metric integrating the curvature information.…”

“…Both these metrics demonstrated improved performance. Several authors also computed 2D image metrics on a set of snapshots around the point clouds and reported correct correlations with subjective scores [26]- [28]. However, this kind of projectionbased approaches have the drawback of being hardly dependent of the rendering used to create the 2D views.…”

“…However, this kind of projectionbased approaches have the drawback of being hardly dependent of the rendering used to create the 2D views. Surprisingly, whereas several subjective studies involve colored point clouds [8], [23], [26], [28], [29], no attempt has yet be made to create a quality metric handling both geometry and color attributes. Note that several quality metrics have also been designed to address 3D content represented by triangulated surfaces.…”

Visual Quality of Compressed Mesh and Point Cloud Sequences

“…For computable metrics of compressed PCs, similar to [7], [8], simple point-to-point and point-to-plane FR objective metrics, such as RMS and HD, showed no correlation with perceptual quality [9]- [12]. The conclusion that pointto-point and point-to-plane metrics are limited in predicting subjective quality ratings especially for TMC2 was also verified in [49], [50]. In [12], point-to-point and point-to-plane metrics were studied for a PC de-noising algorithm; they concluded that the point-to-plane metric is more correlated with perceptual quality than is a point-to-point metric for PC with no noise.…”