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

TL;DR

This paper introduces BASICS, a large-scale dataset for assessing the quality of static point clouds, enabling improved development of objective quality metrics through extensive data collection and benchmarking.

Contribution

The paper presents the BASICS dataset, the first large-scale quality assessment dataset for static point clouds, including diverse compression algorithms and extensive subjective evaluations.

Findings

Benchmarking of existing quality metrics reveals their limitations.

The dataset facilitates future research in developing more accurate quality assessment methods.

Public release of BASICS supports community efforts in point cloud quality evaluation.

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.