DPCD: A Quality Assessment Database for Dynamic Point Clouds

TL;DR

This paper introduces DPCD, a large-scale database for evaluating the quality of dynamic point clouds, facilitating research in DPC quality assessment and supporting development of quality-oriented VR/AR applications.

Contribution

It presents the first comprehensive DPCQA database with subjective scores, enabling systematic evaluation of quality metrics for dynamic point clouds.

Findings

DPCQA is more challenging than static point cloud quality assessment.

The database includes 15 reference and 525 distorted DPCs with subjective scores.

Objective metrics show varying performance on DPCD, highlighting the need for better assessment methods.

Abstract

Recently, the advancements in Virtual/Augmented Reality (VR/AR) have driven the demand for Dynamic Point Clouds (DPC). Unlike static point clouds, DPCs are capable of capturing temporal changes within objects or scenes, offering a more accurate simulation of the real world. While significant progress has been made in the quality assessment research of static point cloud, little study has been done on Dynamic Point Cloud Quality Assessment (DPCQA), which hinders the development of quality-oriented applications, such as interframe compression and transmission in practical scenarios. In this paper, we introduce a large-scale DPCQA database, named DPCD, which includes 15 reference DPCs and 525 distorted DPCs from seven types of lossy compression and noise distortion. By rendering these samples to Processed Video Sequences (PVS), a comprehensive subjective experiment is conducted to obtain Mean Opinion Scores (MOS) from 21 viewers for analysis. The characteristic of contents, impact of various distortions, and accuracy of MOSs are presented to validate the heterogeneity and reliability of the proposed database. Furthermore, we evaluate the performance of several objective metrics on DPCD. The experiment results show that DPCQA is more challenge than that of static point cloud. The DPCD, which serves as a catalyst for new research endeavors on DPCQA, is publicly available at https://huggingface.co/datasets/Olivialyt/DPCD.