  Large garages are ubiquitous yet intricate scenes that present unique
challenges due to their monotonous colors, repetitive patterns, reflective
surfaces, and transparent vehicle glass. Conventional Structure from Motion
(SfM) methods for camera pose estimation and 3D reconstruction often fail in
these environments due to poor correspondence construction. To address these
challenges, we introduce LetsGo, a LiDAR-assisted Gaussian splatting framework
for large-scale garage modeling and rendering. We develop a handheld scanner,
Polar, equipped with IMU, LiDAR, and a fisheye camera, to facilitate accurate
data acquisition. Using this Polar device, we present the GarageWorld dataset,
consisting of eight expansive garage scenes with diverse geometric structures,
which will be made publicly available for further research. Our approach
demonstrates that LiDAR point clouds collected by the Polar device
significantly enhance a suite of 3D Gaussian splatting algorithms for garage
scene modeling and rendering. We introduce a novel depth regularizer that
effectively eliminates floating artifacts in rendered images. Additionally, we
propose a multi-resolution 3D Gaussian representation designed for
Level-of-Detail (LOD) rendering. This includes adapted scaling factors for
individual levels and a random-resolution-level training scheme to optimize the
Gaussians across different resolutions. This representation enables efficient
rendering of large-scale garage scenes on lightweight devices via a web-based
renderer. Experimental results on our GarageWorld dataset, as well as on
ScanNet++ and KITTI-360, demonstrate the superiority of our method in terms of
rendering quality and resource efficiency.
