QAVA-DPC: Eye-Tracking Based Quality Assessment and Visual Attention Dataset for Dynamic Point Cloud in 6 DoF

Perceptual quality assessment of Dynamic Point Cloud (DPC) contents plays an important role in various Virtual Reality (VR) applications that involve human beings as the end user, understanding and modeling perceptual quality assessment is greatly enriched by insights from visual attention. However, incorporating aspects of visual attention in DPC quality models is largely unexplored, as ground-truth visual attention data is scarcely available. This paper presents a dataset containing subjective opinion scores and visual attention maps of DPCs, collected in a VR environment using eye-tracking technology. The data was collected during a subjective quality assessment experiment, in which subjects were instructed to watch and rate DPCs at various degradation levels under 6 degrees-of-freedom inspection, using a head-mounted display. The dataset comprises 5 reference DPC contents, with each reference encoded at 3 distortion levels using 3 different codecs, amounting to a total of 9 degraded DPC contents. Moreover, it includes 1,000 gaze trials from 40 participants, resulting in 15,000 visual attention maps in total. The curated dataset can serve as authentic benchmark data for assessing the performance of objective DPC quality metrics. Additionally, it establishes a link between quality assessment and visual attention within the context of DPC. This work deepens our understanding of DPC quality and visual attention, driving progress in the realm of VR experiences and perception.