Compression Approaches for LiDAR Point Clouds and Beyond: A Survey

With the widespread use of LiDAR sensors in autonomous driving, LiDAR point cloud compression (LPCC) plays an important role in effectively managing the storage, transmission, and perception of the growing volume of LiDAR data. Despite this need, there has been a noticeable absence of comprehensive investigations specifically dedicated to LPCC methods. To address this issue, this article presents a systematic survey of existing LPCCs, aiming to summarize recent progress and inspire future research in this field. We begin by providing a general introduction of LPCC fundamentals, covering the latest LiDAR point cloud (LPC) datasets, distinctive attributes, evaluation metrics, and data formats. We then conduct a careful review and comparison of LPCCs, examining image-based, octree-based, deep-learned, and other approaches, offering valuable insights into the strengths and weaknesses of cutting-edge models. Finally, we propose future research directions based on the limitations of recent LPCCs. We believe that the findings presented in this article will contribute to a deeper understanding of LPCCs and promote further development of LiDAR sensor-based systems.