We introduce a novel large-scale scene reconstruction benchmark that utilizes
newly developed 3D representation approaches: Gaussian Splatting and Neural
Radiance Fields, on our expansive GauU-Scene V2 dataset. GauU-Scene V2
encompasses over 6.5 square kilometers and features a comprehensive RGB dataset
coupled with LiDAR ground truth. This dataset offers a unique blend of urban
and academic environments for advanced spatial analysis, covering more than 6.5
km2. We also provide detailed supplementary information on data collection
protocols. Furthermore, we present an easy-to-follow pipeline to align the
COLMAP sparse point cloud with the detailed LiDAR dataset. Our evaluation of
U-Scene, which includes a detailed analysis across various novel viewpoints
using image-based metrics such as SSIM, LPIPS, and PSNR, shows contradictory
results when applying geometric-based metrics, such as Chamfer distance. This
leads to doubts about the reliability of current image-based measurement
matrices and geometric extraction methods on Gaussian Splatting. We also make
the dataset available on the following anonymous project page

介绍了一种利用 Gaussian Splatting 和 Neural Radiance Fields 进行大规模景观重建的新型基准测试，结合 LiDAR 数据集，涵盖了超过 6.5 平方公里的城市和学术环境，评估了几种度量方法在 Gaussian Splatting 上的可靠性。