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.

翻译：大型停车场是日常生活中普遍存在且结构复杂的场景，其具有颜色单调、纹理重复、表面反光及车辆玻璃透明等特性，给传统运动恢复结构（SfM）方法在相机位姿估计与三维重建中带来了挑战。针对这些问题，本文提出了LetsGo——一种基于LiDAR辅助高斯泼溅的大规模停车场建模与渲染方法。我们研发了配备惯性测量单元（IMU）、LiDAR和鱼眼相机的手持扫描仪Polar，以实现高精度LiDAR与图像数据采集。借助该设备，我们构建了包含五个不同几何结构的大型停车场场景的GarageWorld数据集，并将开源该数据集以促进相关研究。实验证明，Polar设备采集的LiDAR点云能够增强多种三维高斯泼溅算法在停车场场景建模与渲染中的性能。同时，我们提出一种面向三维高斯泼溅算法训练的新型深度正则化器，有效消除渲染图像中的漂浮伪影，并设计轻量级细节层次（LOD）高斯渲染器，支持基于Web设备的实时浏览。此外，我们探索了一种混合表示方法：将传统网格在简单几何与颜色（如墙面和地面）表达中的优势，与捕捉复杂细节及高频纹理的现代三维高斯表示相结合，在内存性能与渲染质量间实现了最优平衡。在GarageWorld数据集及ScanNet++、KITTI-360上的实验结果表明，本方法在渲染质量和资源效率方面均具有显著优势。