  This paper aims to tackle the problem of modeling dynamic urban streets for
autonomous driving scenes. Recent methods extend NeRF by incorporating tracked
vehicle poses to animate vehicles, enabling photo-realistic view synthesis of
dynamic urban street scenes. However, significant limitations are their slow
training and rendering speed. We introduce Street Gaussians, a new explicit
scene representation that tackles these limitations. Specifically, the dynamic
urban scene is represented as a set of point clouds equipped with semantic
logits and 3D Gaussians, each associated with either a foreground vehicle or
the background. To model the dynamics of foreground object vehicles, each
object point cloud is optimized with optimizable tracked poses, along with a 4D
spherical harmonics model for the dynamic appearance. The explicit
representation allows easy composition of object vehicles and background, which
in turn allows for scene editing operations and rendering at 135 FPS (1066
$\times$ 1600 resolution) within half an hour of training. The proposed method
is evaluated on multiple challenging benchmarks, including KITTI and Waymo Open
datasets. Experiments show that the proposed method consistently outperforms
state-of-the-art methods across all datasets. The code will be released to
ensure reproducibility.
