  Point cloud compression has garnered significant interest in computer vision.
However, existing algorithms primarily cater to human vision, while most point
cloud data is utilized for machine vision tasks. To address this, we propose a
point cloud compression framework that simultaneously handles both human and
machine vision tasks. Our framework learns a scalable bit-stream, using only
subsets for different machine vision tasks to save bit-rate, while employing
the entire bit-stream for human vision tasks. Building on mainstream
octree-based frameworks like VoxelContext-Net, OctAttention, and G-PCC, we
introduce a new octree depth-level predictor. This predictor adaptively
determines the optimal depth level for each octree constructed from a point
cloud, controlling the bit-rate for machine vision tasks. For simpler tasks
(\textit{e.g.}, classification) or objects/scenarios, we use fewer depth levels
with fewer bits, saving bit-rate. Conversely, for more complex tasks
(\textit{e.g}., segmentation) or objects/scenarios, we use deeper depth levels
with more bits to enhance performance. Experimental results on various datasets
(\textit{e.g}., ModelNet10, ModelNet40, ShapeNet, ScanNet, and KITTI) show that
our point cloud compression approach improves performance for machine vision
tasks without compromising human vision quality.
