Point Cloud Compression and Objective Quality Assessment: A Survey
Journal:
arXiv
Published Date:
Jun 28, 2025
Abstract
The rapid growth of 3D point cloud data, driven by applications in autonomous
driving, robotics, and immersive environments, has led to criticals demand for
efficient compression and quality assessment techniques. Unlike traditional 2D
media, point clouds present unique challenges due to their irregular structure,
high data volume, and complex attributes. This paper provides a comprehensive
survey of recent advances in point cloud compression (PCC) and point cloud
quality assessment (PCQA), emphasizing their significance for real-time and
perceptually relevant applications. We analyze a wide range of handcrafted and
learning-based PCC algorithms, along with objective PCQA metrics. By
benchmarking representative methods on emerging datasets, we offer detailed
comparisons and practical insights into their strengths and limitations.
Despite notable progress, challenges such as enhancing visual fidelity,
reducing latency, and supporting multimodal data remain. This survey outlines
future directions, including hybrid compression frameworks and advanced feature
extraction strategies, to enable more efficient, immersive, and intelligent 3D
applications.
