BASICS: Broad quality Assessment of Static point clouds In Compression Scenarios

Abstract

Point clouds have become increasingly prevalent in representing 3D scenes within virtual environments, alongside 3D meshes. Their ease of capture has facilitated a wide array of applications on mobile devices, from smartphones to autonomous vehicles. Notably, point cloud compression has reached an advanced stage and has been standardized. However, the availability of quality assessment datasets, which are essential for developing improved objective quality metrics, remains limited. In this paper, we introduce BASICS, a large-scale quality assessment dataset tailored for static point clouds. The BASICS dataset comprises 75 unique point clouds, each compressed with four different algorithms including a learning-based method, resulting in the evaluation of nearly 1500 point clouds by 3500 unique participants. Furthermore, we conduct a comprehensive analysis of the gathered data, benchmark existing point cloud quality assessment metrics and identify their limitations. By publicly releasing the BASICS dataset, we lay the foundation for addressing these limitations and fostering the development of more precise quality metrics.

Published in IEEE TMM, 14 pages, 8 figures

References in corpus (1)

• Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display

Cited by in corpus (3)

• No-reference geometry quality assessment for colorless point clouds via list-wise rank learning
• Low-Complexity Patch-based No-Reference Point Cloud Quality Metric exploiting Weighted Structure and Texture Features
• RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation