<p>This paper addresses the pressing need for efficient compression techniques for 3D point cloud data, which is crucial for both human–computer interaction and machine vision tasks. While existing methods often prioritize human perception, they fail to meet the demands of machine-driven applications, leading to data redundancy. We introduce a collaborative point cloud geometry compression approach that optimally balances human and machine vision tasks. Leveraging global and local features, our method minimizes redundancy across various tasks, achieved through a dual-branch architecture for feature extraction and entropy encoding. Another dual-branch structure facilitates high-fidelity point cloud reconstruction and machine vision tasks during decoding, with a task-friendly entropy engine enhancing coding efficiency. Our contributions include a comprehensive framework for human–machine collaborative compression, a novel dual-branch feature extraction encoder, and a coordinate reconstruction module for decoding and fusion. Extensive experiments validate the superiority of our method, promising significant reductions in bit rate while maintaining reconstruction quality and machine vision performance. </p>