Reconstructing dynamic 3D urban scenes is crucial for autonomous driving, yet current methods face a stark trade-off between fidelity and computational cost. This inefficiency stems from their semantically agnostic design, which allocates resources uniformly, treating static backgrounds and safety-critical objects with equal importance. To address this, we introduce Priority-Adaptive Gaussian Splatting (PAGS), a framework that injects task-aware semantic priorities directly into the 3D reconstruction and rendering pipeline. PAGS introduces two core contributions: (1) Semantically-Guided Pruning and Regularization strategy, which employs a hybrid importance metric to aggressively simplify non-critical scene elements while preserving fine-grained details on objects vital for navigation. (2) Priority-Driven Rendering pipeline, which employs a priority-based depth pre-pass to aggressively cull occluded primitives and accelerate the final shading computations. Extensive experiments on the Waymo and KITTI datasets demonstrate that PAGS achieves exceptional reconstruction quality, particularly on safety-critical objects, while significantly reducing training time and boosting rendering speeds to over 350 FPS.

翻译：动态三维城市场景重建对于自动驾驶至关重要，然而现有方法在保真度与计算成本之间存在显著的权衡。这种低效性源于其语义无关的设计，即均匀分配计算资源，对静态背景与安全关键对象给予同等重要性。为解决此问题，我们提出了优先级自适应高斯泼溅（PAGS），该框架将任务感知的语义优先级直接注入三维重建与渲染流程。PAGS包含两项核心贡献：（1）语义引导的剪枝与正则化策略，采用混合重要性度量，在保留对导航至关重要的对象细粒度细节的同时，对非关键场景元素进行大幅简化。（2）优先级驱动的渲染流程，采用基于优先级的深度预渲染通道，以高效剔除被遮挡的图元并加速最终着色计算。在Waymo和KITTI数据集上的大量实验表明，PAGS实现了卓越的重建质量，尤其在安全关键对象上表现突出，同时显著减少了训练时间，并将渲染速度提升至超过350 FPS。