近年3D数据采集和处理技术快速发展，点云作为一种典型的3D媒体数据类型引起越来越多的关注，其在无人驾驶、混合现实、测绘和医学影像等多个领域的发展上起到了巨大作用。与传统的图像、视频类似，点云在计算处理过程中会不可避免的引入各种失真，因此，合理准确的点云质量评价模型尤为重要。本文首先分析点云质量评价存在的挑战与困难；然后从点云主观实验方法、数据库构建和客观模型设计三个方面阐述研究现状；针对其关键技术进行分析和探索，并介绍本团队在点云主观质量评价标准化、数据库构建和客观模型设计上取得的阶段成果和未来展望。

In recent years, point clouds have attracted more attention in the application of autonomous driving, virtual reality, and industrial robots due to the development of 3D data acquisition and processing. Similar to traditional images and videos, point clouds are inevitably injected various distortions during processing, which means point cloud quality assessment is an important issue for practical application. This paper will demonstrate the challenges and difficulties of point cloud quality assessment, and review the achievement of point cloud subjective quality evaluation method, database construction, and objective model design. Besides, we introduce the latest research fruits contributed by our team, and the future development of point cloud quality assessment.