  Modeling dynamic, large-scale urban scenes is challenging due to their highly
intricate geometric structures and unconstrained dynamics in both space and
time. Prior methods often employ high-level architectural priors, separating
static and dynamic elements, resulting in suboptimal capture of their
synergistic interactions. To address this challenge, we present a unified
representation model, called Periodic Vibration Gaussian (PVG). PVG builds upon
the efficient 3D Gaussian splatting technique, originally designed for static
scene representation, by introducing periodic vibration-based temporal
dynamics. This innovation enables PVG to elegantly and uniformly represent the
characteristics of various objects and elements in dynamic urban scenes. To
enhance temporally coherent and large scene representation learning with sparse
training data, we introduce a novel temporal smoothing mechanism and a
position-aware adaptive control strategy respectively. Extensive experiments on
Waymo Open Dataset and KITTI benchmarks demonstrate that PVG surpasses
state-of-the-art alternatives in both reconstruction and novel view synthesis
for both dynamic and static scenes. Notably, PVG achieves this without relying
on manually labeled object bounding boxes or expensive optical flow estimation.
Moreover, PVG exhibits 900-fold acceleration in rendering over the best
alternative.
