HM-PCGC: A Human-Machine Balanced Point Cloud Geometry Compression Scheme

Abstract

Point cloud compression has various purposes in different application scenarios, such as requiring visual fidelity in human vision tasks and pursuing semantic fidelity in machine vision tasks. To accommodate these diverse requirements, we propose a Human-Machine balanced point cloud geometry compression scheme (HM-PCGC) which considers the characteristics of various tasks. Our proposed scheme starts from a pre-trained, lightweight point cloud compression backbone and employs a Learned Semantic Mining module to aggregate multi-tasks features. By leveraging the aggregated features, HM-PCGC is able to retain the geometry and semantic properties of the point clouds during compression. To better balance between the signal distortion and semantic distortion, we integrated a multi-task learning mechanism during the training phase. Our approach is extensively evaluated and analyzed, and the results demonstrate its superiority over state-of-the-art traditional and deep learning based point cloud codecs for both signal reconstruction and machine vision tasks.