  Unveiling an empty street from crowded observations captured by in-car
cameras is crucial for autonomous driving. However, removing all temporarily
static objects, such as stopped vehicles and standing pedestrians, presents a
significant challenge. Unlike object-centric 3D inpainting, which relies on
thorough observation in a small scene, street scene cases involve long
trajectories that differ from previous 3D inpainting tasks. The camera-centric
moving environment of captured videos further complicates the task due to the
limited degree and time duration of object observation. To address these
obstacles, we introduce StreetUnveiler to reconstruct an empty street.
StreetUnveiler learns a 3D representation of the empty street from crowded
observations. Our representation is based on the hard-label semantic 2D
Gaussian Splatting (2DGS) for its scalability and ability to identify Gaussians
to be removed. We inpaint rendered image after removing unwanted Gaussians to
provide pseudo-labels and subsequently re-optimize the 2DGS. Given its temporal
continuous movement, we divide the empty street scene into observed,
partial-observed, and unobserved regions, which we propose to locate through a
rendered alpha map. This decomposition helps us to minimize the regions that
need to be inpainted. To enhance the temporal consistency of the inpainting, we
introduce a novel time-reversal framework to inpaint frames in reverse order
and use later frames as references for earlier frames to fully utilize the
long-trajectory observations. Our experiments conducted on the street scene
dataset successfully reconstructed a 3D representation of the empty street. The
mesh representation of the empty street can be extracted for further
applications. The project page and more visualizations can be found at:
https://streetunveiler.github.io
