使用Gaussian Splatting和Google Earth影像增强3D城市场景重建与点云密集化
Enhanced 3D Urban Scene Reconstruction and Point Cloud Densification using Gaussian Splatting and Google Earth Imagery
AI总结：
本研究利用Google Earth影像构建3D Gaussian Splatting模型，实现了远超神经辐射场的照片级城市场景视图合成，并通过提取点云与多视图立体重建对比，完成了大规模场景的3D几何与光照重建。
摘要：
3D城市场景重建与建模是遥感领域的关键研究方向，在学术、商业、工业和行政管理中具有广泛应用。视图合成模型的最新进展使得仅从2D图像实现照片级真实感3D重建成为可能。利用Google Earth影像，我们构建了以滑铁卢大学为中心的滑铁卢地区3D Gaussian Splatting模型，并在基准测试中证明其视图合成结果远超以往基于神经辐射场的3D视图合成结果。此外，我们通过从3D Gaussian Splatting模型提取的3D点云检索场景的3D几何结构，并将其与我们的多视图立体密集重建结果进行基准对比，从而通过3D Gaussian Splatting重建了大规模城市场景的3D几何结构与照片级真实感光照。
英文摘要：
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
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