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.

翻译：点云压缩在计算机视觉领域引起了广泛关注。然而，现有算法主要面向人类视觉，而大部分点云数据被用于机器视觉任务。为解决这一问题，我们提出了一种同时处理人类视觉与机器视觉任务的点云压缩框架。该框架学习可扩展的比特流，针对不同机器视觉任务仅使用其子集以节省比特率，而对人类视觉任务则使用完整的比特流。基于VoxelContext-Net、OctAttention和G-PCC等主流八叉树框架，我们引入了一种新的八叉树深度级别预测器。该预测器能自适应地确定由点云构建的每个八叉树的最佳深度级别，从而控制机器视觉任务的比特率。对于较简单的任务（例如分类）或对象/场景，我们使用较少的深度级别和较少的比特数以节省比特率；反之，对于更复杂的任务（例如分割）或对象/场景，则使用更深的深度级别和更多的比特数以提升性能。在多个数据集（例如ModelNet10、ModelNet40、ShapeNet、ScanNet和KITTI）上的实验结果表明，我们的点云压缩方法在保持人类视觉质量的同时，有效提升了机器视觉任务的性能。