光学卫星图像三维景物结构的仿射重建到欧氏重建
Pursuing 3D Scene Structures with Optical Satellite Images from Affine Reconstruction to Euclidean Reconstruction
摘要：
如何利用多幅光学卫星图像恢复场景的三维结构是遥感领域一个具有挑战性的重要问题。现有的大多数方法都是基于经典的RPC（rational polynomial camera）模型，该模型需要至少39个地面控制点，然而，在许多真实场景中要获得如此多的地面控制点并不容易。针对这一问题，我们提出了一个分层重建框架的基础上，多个光学卫星图像，它只需要4个控制点。该框架由仿射稠密重建阶段和仿射到欧氏升级阶段组成：在仿射稠密重建阶段，探索了一种仿射稠密重建方法，用于在没有任何GCP的情况下从输入卫星图像中追求3D仿射场景结构。然后在仿射到欧几里德升级阶段，将获得的3D仿射结构升级为具有4个GCP的欧几里德结构。在两个公开数据集上的实验结果表明，该方法在大多数情况下明显优于三种最先进的方法。
英文摘要：
How to use multiple optical satellite images to recover the 3D scene structure is a challenging and important problem in the remote sensing field. Most existing methods in literature have been explored based on the classical RPC (rational polynomial camera) model which requires at least 39 GCPs (ground control points), however, it is not trivial to obtain such a large number of GCPs in many real scenes. Addressing this problem, we propose a hierarchical reconstruction framework based on multiple optical satellite images, which needs only 4 GCPs. The proposed framework is composed of an affine dense reconstruction stage and a followed affine-to-Euclidean upgrading stage: At the affine dense reconstruction stage, an affine dense reconstruction approach is explored for pursuing the 3D affine scene structure without any GCP from input satellite images. Then at the affine-to-Euclidean upgrading stage, the obtained 3D affine structure is upgraded to a Euclidean one with 4 GCPs. Experimental results on two public datasets demonstrate that the proposed method significantly outperforms three state-of-the-art methods in most cases.
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