Zhang, XinranZhu, HanqiDuan, YifanZhang, YanyongChristie, MarcPietroni, NicoWang, Yu-Shuen2025-10-072025-10-0720251467-8659https://doi.org/10.1111/cgf.70248https://diglib.eg.org/handle/10.1111/cgf70248Constructing and sharing 3D maps is essential for many applications, including autonomous driving and augmented reality. Recently, 3D Gaussian splatting has emerged as a promising approach for accurate 3D reconstruction. However, a practical map-sharing system that features high-fidelity, continuous updates, and network efficiency remains elusive. To address these challenges, we introduce GS-Share, a photorealistic map-sharing system with a compact representation. The core of GS-Share includes anchor-based global map construction, virtual-image-based map enhancement, and incremental map update. We evaluate GS-Share against state-of-the-art methods, demonstrating that our system achieves higher fidelity, particularly for extrapolated views, with improvements of 11%, 22%, and 74% in PSNR, LPIPS, and Depth L1, respectively. Furthermore, GS-Share is significantly more compact, reducing map transmission overhead by 36%.CCS Concepts: Computing methodologies → Rendering; Shape modeling; Information systems → Data compressionComputing methodologies → RenderingShape modelingInformation systems → Data compression10.1111/cgf.7024811 pages