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.
大型车库是我们日常生活中无处不在但又复杂的场景，由于单调的颜色、重复的图案、反光表面和透明的车辆玻璃，它们带来了特有的挑战。传统的运动恢复结构（Structure from Motion, SfM）方法在这些环境中因对应构建不佳而失败。为了应对这些挑战，本文介绍了LetsGo，一种激光雷达辅助的高斯飞溅方法，用于大规模车库建模和渲染。我们开发了一种手持扫描器Polar，配备了IMU、激光雷达和鱼眼相机，以便进行精确的激光雷达和图像数据扫描。利用这种Polar设备，我们呈现了一个包含五个具有不同几何结构的广阔车库场景的GarageWorld数据集，并将发布该数据集供社区进一步研究。我们展示了由Polar设备收集的激光雷达点云如何增强一套3D高斯飞溅算法，用于车库场景的建模和渲染。我们还提出了一个新颖的深度正则化器，用于3D高斯飞溅算法的训练，有效消除渲染图像中的浮动伪影，并提出了一个轻量级的细节级别（Level of Detail, LOD）高斯渲染器，用于基于Web的设备上的实时查看。此外，我们探索了一种混合表现形式，它结合了传统网格在描述简单几何和颜色（如墙壁和地面）方面的优势与现代3D高斯表现形式捕捉复杂细节和高频纹理的能力。这种策略实现了内存性能和渲染质量之间的最佳平衡。我们在自己的数据集以及ScanNet++和KITTI-360上的实验结果展示了我们方法在渲染质量和资源效率方面的优越性。