Learning-based point cloud perception methods rely on labeled data for training data-driven models, necessi-tating the development of precise and efficient tools for point cloud annotations. In this paper, we propose MAVR, a multi-functional annotation framework based on virtual reality (VR) technology, capable of accurately labeling point cloud data for diverse applications, including part segmentation and object detection. We begin by evaluating the user interface (UI) efficiency through interactive efficiency analysis. Subsequently, a comprehensive three-step process is introduced, which consists of pre-processing, point selection, and post-tagging. For 3D object part segmentation and scene perception, we propose two distinct tagging pipelines. Our experimental results on various datasets validate the effectiveness of MAVR in accurately annotating point clouds from different data sources within an immersive workspace.