We present BlitzGS, a distributed 3DGS framework that reduces active Gaussian workload for fast city-scale reconstruction. BlitzGS manages this workload at three coupled levels. At the system level, the framework shards Gaussians across GPUs by index parity rather than spatial blocks. This approach mitigates the cross-block visibility redundancy inherent in spatial partitioning. Furthermore, it distributes each rendering step through a single cross-GPU exchange that routes projected Gaussians to their tile owners. At the model level, scheduled importance-scoring passes shrink the global Gaussian population. During these passes, the framework generates a per-Gaussian visibility weight to bias density-control updates toward contributing primitives and a per-view importance mask for the view-level renderer. At the view level, BlitzGS trims each camera's active set with a distance-based LOD gate to exclude excessively fine primitives for the current frustum and the importance-based culling mask to skip Gaussians with negligible cross-view contribution. On large-scale benchmarks, BlitzGS matches the rendering quality of recent large-scale baselines while delivering an order-of-magnitude speedup, training city-scale scenes in tens of minutes. Our code is available at https: //github.com/AkierRaee/BlitzGS.

翻译：我们提出BlitzGS，一种分布式3DGS框架，通过减少活跃高斯原语的工作负载实现快速城市级重建。BlitzGS在三个耦合层面管理工作负载。系统层面，该框架依据索引奇偶性而非空间区块对GPU间的高斯原语进行分片。此方法缓解了空间分区固有的跨块可见性冗余，并通过单次跨GPU交换分配每个渲染步骤，将投影后的高斯原语路由至其所属图块。模型层面，计划性重要性评分过程缩减全局高斯群体规模。在这些过程中，框架生成逐高斯可见性权重以偏向密度控制更新于贡献性基元，并生成逐视图重要性掩码供视图级渲染器使用。视图层面，BlitzGS通过基于距离的LOD门裁剪每个相机的活跃集，以排除当前视锥中过度精细的基元，并利用基于重要性的剔除掩码跳过跨视图贡献可忽略的高斯原语。在大型基准测试中，BlitzGS在实现数量级加速的同时匹配近期大规模基线的渲染质量，可在数十分钟内完成城市级场景训练。我们代码见https://github.com/AkierRaee/BlitzGS。