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arXiv 2102.02502cs.CV
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基于多数据卫星影像的三维表面重建

3D Surface Reconstruction From Multi-Date Satellite Images

Fraunhofer IOSB（弗劳恩霍夫光电、系统技术与图像处理研究所）

Sebastian Bullinger, Christoph Bodensteiner, Michael Arens

备注：
Accepted at ISPRS Congress 2021
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摘要：

精确的三维环境模型重建是摄影测量领域最基本的目标之一。由于卫星图像为获得大规模环境重建提供了合适的属性，因此存在多种基于立体匹配的方法来重建卫星图像对的点云。最近，第一个运动恢复结构（SfM）为基础的方法已经提出，它允许从多个卫星图像重建点云。在这项工作中，我们提出了一个扩展的SfM为基础的管道，使我们能够重建不仅点云，但防水网格，包括纹理信息。我们提供了几个步骤，是强制性的，以利用国家的最先进的网格重建算法的卫星图像的背景下的详细描述。这包括有限投影相机校准矩阵的分解、对应深度图和输入图像的偏斜校正以及从重新参数化的深度值恢复真实世界深度图。本文提出了一个广泛的定量评估多日期的卫星图像表明，所提出的管道结合当前的网格算法优于国家的最先进的点云重建算法的完整性和中值误差。我们公开了我们管道的源代码。

英文摘要：

The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry. Since satellite images provide suitable properties for obtaining large-scale environment reconstructions, there exist a variety of Stereo Matching based methods to reconstruct point clouds for satellite image pairs. Recently, the first Structure from Motion (SfM) based approach has been proposed, which allows to reconstruct point clouds from multiple satellite images. In this work, we propose an extension of this SfM based pipeline that allows us to reconstruct not only point clouds but watertight meshes including texture information. We provide a detailed description of several steps that are mandatory to exploit state-of-the-art mesh reconstruction algorithms in the context of satellite imagery. This includes a decomposition of finite projective camera calibration matrices, a skew correction of corresponding depth maps and input images as well as the recovery of real-world depth maps from reparameterized depth values. The paper presents an extensive quantitative evaluation on multi-date satellite images demonstrating that the proposed pipeline combined with current meshing algorithms outperforms state-of-the-art point cloud reconstruction algorithms in terms of completeness and median error. We make the source code of our pipeline publicly available.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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