Abstract: Dynamic point cloud data, characterized by temporal continuity and high-dimensional features, can accu-rately capture dynamic changes of objects and are thus widely applied in fields such as autonomous driv-ing, augmented reality, virtual reality, robotic navigation, and 3D video. Dynamic point cloud compression plays a crucial role in efficiently managing the storage, transmission, and perception of the ever-increasing volume of point cloud data. Despite this demand, there remains a clear lack of comprehensive surveys spe-cifically focused on dynamic point cloud compression methods. To address this gap, this paper presents a systematic review of existing approaches, aiming to summarize recent advancements in the field and pro-vide insights for future research. The paper first introduces the significance and theoretical foundations of dynamic point cloud compression. It then elaborates on the fundamental principles and advantages of mainstream models from three perspectives: geometry compression, attribute compression, and joint com-pression. Subsequently, commonly used datasets and evaluation metrics are reviewed, and the performance of various methods across different datasets is summarized. Finally, the paper identifies current limitations in dynamic point cloud compression research and outlines promising future directions. This survey is ex-pected to facilitate a deeper understanding of dynamic point cloud compression and promote further de-velopment of related systems.