论文标题
意义：来自具有深层隐性占用领域的卫星图像的城市建模
ImpliCity: City Modeling from Satellite Images with Deep Implicit Occupancy Fields
论文作者
论文摘要
高分辨率的光学卫星传感器，结合密集的立体声算法，使得可以从空间重建3D城市模型。但是，在实践中，这些模型是嘈杂的，并且往往会错过图像中清晰可见的小几何特征。我们认为，有限质量的原因可能是太早，将三角构云的3D点云降低到明显的高度场或表面网格。为了充分利用点云和基础图像，我们介绍了Incantity，这是3D场景的神经表示形式，是一个隐含的，连续的占用场，由学到的点云的嵌入和立体声的正式孔子驱动。我们表明，该表示形式可以提取高质量的DSM：使用图像分辨率为0.5 $ \，$ m，Inctity达到了中间的高度误差$ \ of $ \ of $ \ of $ 0.7 $ \，$ m，$ M，均超过竞争方法，尤其是W.R.T.建筑重建，具有复杂的屋顶细节，光滑的表面和直截了当的轮廓。
High-resolution optical satellite sensors, combined with dense stereo algorithms, have made it possible to reconstruct 3D city models from space. However, these models are, in practice, rather noisy and tend to miss small geometric features that are clearly visible in the images. We argue that one reason for the limited quality may be a too early, heuristic reduction of the triangulated 3D point cloud to an explicit height field or surface mesh. To make full use of the point cloud and the underlying images, we introduce ImpliCity, a neural representation of the 3D scene as an implicit, continuous occupancy field, driven by learned embeddings of the point cloud and a stereo pair of ortho-photos. We show that this representation enables the extraction of high-quality DSMs: with image resolution 0.5$\,$m, ImpliCity reaches a median height error of $\approx\,$0.7$\,$m and outperforms competing methods, especially w.r.t. building reconstruction, featuring intricate roof details, smooth surfaces, and straight, regular outlines.
