This chapter discusses human and machine perception oriented point cloud coding technology, focusing on how to meet the needs of human visual perception and machine vision perception with the restricted bandwidth. Traditional point cloud coding techniques primarily optimize signal fidelity and human visual perception. However, as machine vision tasks become increasingly prevalent, new methods have been developed to optimize the machine perception performance. This chapter provides a detailed overview of several innovative point cloud coding frameworks, which enhance compression performance and task efficiency through techniques like octree depth prediction, semantic information extraction, and dual-branch architectures. It also explores how adaptive coding strategy can better support real-time processing for specific application scenarios, such as LiDAR point cloud coding in autonomous driving. Overall, this chapter summarizes the latest advancements in the field of point cloud coding and proposes potential future research directions.