This chapter introduces the fundamentals of 3D point cloud coding, covering key aspects including 3D visual data formats, widely used point cloud datasets, traditional non-learning-based compression standards, and emerging AI-driven approaches. First, various 3D visual data formats are analyzed, highlighting the advantages of point clouds in explicitly representing 3D geometry structures and associated attributes. Afterward, representative point cloud datasets and their applications are reviewed. The discussion then addresses the necessity of 3D point cloud coding, examining key challenges and the growing shift toward AI-based methods that demonstrate superior efficiency and reconstruction quality. Additionally, quality assessment methodologies are examined, including both subjective evaluations and objective metrics. By establishing this foundation, the chapter provides essential context for understanding the problem of point cloud coding and the transformative potential of AI in this domain.