  We introduce a novel, multimodal large-scale scene reconstruction benchmark
that utilizes newly developed 3D representation approaches: Gaussian Splatting
and Neural Radiance Fields (NeRF). Our expansive U-Scene dataset surpasses any
previously existing real large-scale outdoor LiDAR and image dataset in both
area and point count. GauU-Scene encompasses over 6.5 square kilometers and
features a comprehensive RGB dataset coupled with LiDAR ground truth.
Additionally, we are the first to propose a LiDAR and image alignment method
for a drone-based dataset. Our assessment of GauU-Scene includes a detailed
analysis across various novel viewpoints, employing image-based metrics such as
SSIM, LPIPS, and PSNR on NeRF and Gaussian Splatting based methods. This
analysis reveals contradictory results when applying geometric-based metrics
like Chamfer distance. The experimental results on our multimodal dataset
highlight the unreliability of current image-based metrics and reveal
significant drawbacks in geometric reconstruction using the current Gaussian
Splatting-based method, further illustrating the necessity of our dataset for
assessing geometry reconstruction tasks. We also provide detailed supplementary
information on data collection protocols and make the dataset available on the
following anonymous project page
