系统梳理点云压缩与质量评估技术，助力3D应用高效落地
Point Cloud Compression and Objective Quality Assessment: A Survey
- 对比手工设计与学习型压缩算法，分析其优劣
- 在新数据集上评测主流方法，揭示性能瓶颈
- 适合关注3D视觉、自动驾驶和元宇宙的开发者
随着自动驾驶、机器人和沉浸式环境等应用推动3D点云数据快速增长，高效压缩与质量评估技术需求迫切。相较于传统2D媒体，点云因结构不规则、数据量大、属性复杂而面临独特挑战。本文全面综述了点云压缩（PCC）与点云质量评估（PCQA）的最新进展，强调其在实时性与感知相关性应用中的重要性。我们分析了大量手工设计与基于学习的PCC算法，以及客观PCQA度量方法，并通过在新兴数据集上的基准测试，提供详细的性能对比与实践洞察。尽管取得显著进展，提升视觉保真度、降低延迟、支持多模态数据仍是主要挑战。本文还指明未来方向，包括混合压缩框架与先进特征提取策略，以推动更高效、沉浸式、智能化的3D应用发展。
原文摘要 · Abstract (English)
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.
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