GeoQE: Enhancing Quality of Experience in Point Cloud Streaming
Junzhe Zhang, Chengfeng Han, Dandan Ding, Zhan Ma
Abstract
Point cloud compression (PCC) is indispensable for the upcoming holographic communication, enabling efficient transmission and real-time interaction with high-fidelity 3D data. As a mature international PCC standard, MPEG G-PCC holds promise for widespread applications due to its support for various point cloud types and ease of implementation. However, the geometry compression performance of G-PCC is limited, which severely impacts the user quality of experience (QoE). To address this, we propose GeoQE, an enhancement model that seamlessly integrates with the G-PCC decoder to mitigate compression artifacts and improve QoE. GeoQE introduces two key techniques: (1) a quantizer-guided expansion operation that adaptively handles distortions at varying levels, and (2) a spatiotemporal mechanism that leverages correlations within the current frame and across adjacent frames, allowing effective enhancement even with a lightweight network. Experiments show that GeoQE delivers state-of-the-art performance on both dense (e.g., VR/AR) and sparse (e.g., LiDAR for autonomous driving) point clouds. Operating at around 4 fps on a 3090Ti GPU with a compact 1.6 MB model, it achieves much lower computational complexity than existing methods, which is attractive for practical applications.
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