LetsGo: Large-Scale Garage Modeling and Rendering via LiDAR-Assisted
  Gaussian Primitives
Jiadi Cui Junming Cao Yuhui Zhong Liao Wang Fuqiang Zhao Penghao Wang Yifan Chen Zhipeng He Lan Xu Yujiao Shi Yingliang Zhang Jingyi Yu
Large garages are ubiquitous yet intricate scenes in our daily lives, posing
challenges characterized by monotonous colors, repetitive patterns, reflective
surfaces, and transparent vehicle glass. Conventional Structure from Motion
(SfM) methods for camera pose estimation and 3D reconstruction fail in these
environments due to poor correspondence construction. To address these
challenges, this paper introduces LetsGo, a LiDAR-assisted Gaussian splatting
approach for large-scale garage modeling and rendering. We develop a handheld
scanner, Polar, equipped with IMU, LiDAR, and a fisheye camera, to facilitate
accurate LiDAR and image data scanning. With this Polar device, we present a
GarageWorld dataset consisting of five expansive garage scenes with diverse
geometric structures and will release the dataset to the community for further
research. We demonstrate that the collected LiDAR point cloud by the Polar
device enhances a suite of 3D Gaussian splatting algorithms for garage scene
modeling and rendering. We also propose a novel depth regularizer for 3D
Gaussian splatting algorithm training, effectively eliminating floating
artifacts in rendered images, and a lightweight Level of Detail (LOD) Gaussian
renderer for real-time viewing on web-based devices. Additionally, we explore a
hybrid representation that combines the advantages of traditional mesh in
depicting simple geometry and colors (e.g., walls and the ground) with modern
3D Gaussian representations capturing complex details and high-frequency
textures. This strategy achieves an optimal balance between memory performance
and rendering quality. Experimental results on our dataset, along with
ScanNet++ and KITTI-360, demonstrate the superiority of our method in rendering
quality and resource efficiency.