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.

中文翻译：

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LetsGo：通过 LiDAR 辅助的高斯基元进行大规模车库建模和渲染


大型车库无处不在但错综复杂的场景，由于其单调的色彩、重复的图案、反光表面和透明的车辆玻璃，带来了独特的挑战。由于通信结构不佳，用于相机姿态估计和 3D 重建的传统运动结构 （SfM） 方法在这些环境中经常失败。为了应对这些挑战，我们推出了 LetsGo，这是一个用于大规模车库建模和渲染的 LiDAR 辅助高斯展开框架。我们开发了一款手持式扫描仪 Polar，配备 IMU、LiDAR 和鱼眼摄像头，以促进准确的数据采集。使用这个 Polar 设备，我们展示了 GarageWorld 数据集，该数据集由八个具有不同几何结构的广阔车库场景组成，这些场景将公开以供进一步研究。我们的方法表明，Polar 设备收集的 LiDAR 点云显著增强了一套用于车库场景建模和渲染的 3D 高斯飞溅算法。我们引入了一种新颖的深度正则化器，可有效消除渲染图像中的浮动伪影。此外，我们提出了一种专为细节级别 （LOD） 渲染而设计的多分辨率 3D 高斯表示。这包括针对各个级别的调整比例因子和随机分辨率级别的训练方案，以优化不同分辨率下的高斯分布。这种表示支持通过基于 Web 的渲染器在轻量级设备上高效渲染大型车库场景。在我们的 GarageWorld 数据集以及 ScanNet++ 和 KITTI-360 上的实验结果表明，我们的方法在渲染质量和资源效率方面具有优越性。