3D urban scene reconstruction and modelling is a crucial research area in
remote sensing with numerous applications in academia, commerce, industry, and
administration. Recent advancements in view synthesis models have facilitated
photorealistic 3D reconstruction solely from 2D images. Leveraging Google Earth
imagery, we construct a 3D Gaussian Splatting model of the Waterloo region
centered on the University of Waterloo and are able to achieve view-synthesis
results far exceeding previous 3D view-synthesis results based on neural
radiance fields which we demonstrate in our benchmark. Additionally, we
retrieved the 3D geometry of the scene using the 3D point cloud extracted from
the 3D Gaussian Splatting model which we benchmarked against our Multi-
View-Stereo dense reconstruction of the scene, thereby reconstructing both the
3D geometry and photorealistic lighting of the large-scale urban scene through
3D Gaussian Splatting

使用 Google Earth 图像构建了基于 3D 高斯光斑模型的滑铲三维重建模型，利用视图合成模型实现了比基于神经辐射场更好的三维视图合成结果，并通过多视图稠密重建对场景的三维几何和照明进行了重建。