This paper aims to develop an accurate 3D geometry representation of satellite images using satellite-ground image pairs. Our focus is on the challenging problem of 3D-aware ground-views synthesis from a satellite image. We draw inspiration from the density field representation used in volumetric neural rendering and propose a new approach, called Sat2Density. Our method utilizes the properties of ground-view panoramas for the sky and non-sky regions to learn faithful density fields of 3D scenes in a geometric perspective. Unlike other methods that require extra depth information during training, our Sat2Density can automatically learn accurate and faithful 3D geometry via density representation without depth supervision. This advancement significantly improves the ground-view panorama synthesis task. Additionally, our study provides a new geometric perspective to understand the relationship between satellite and ground-view images in 3D space.

翻译：本文旨在利用卫星-地面图像对，为卫星图像建立精确的三维几何表达。我们聚焦于从卫星图像进行三维感知的地面视图合成这一挑战性问题。受体积神经渲染中密度场表示的启发，我们提出了一种新方法——Sat2Density。该方法利用地面全景图在天空与非天空区域的特性，从几何视角学习三维场景的忠实密度场。与需要在训练过程中额外深度信息的其他方法不同，我们的Sat2Density无需深度监督即可通过密度表示自动学习精确且忠实的三维几何。这一进展显著提升了地面全景图合成任务。此外，本研究为理解卫星与地面视图图像在三维空间中的关系提供了新的几何视角。