Three-dimensional (3D) surface models, e.g., digital elevation models (DEMs), are important for planetary exploration missions and scientific research. Current DEMs of the Martian surface are mainly generated by laser altimetry or photogrammetry, which have respective limitations. Laser altimetry cannot produce high-resolution DEMs; photogrammetry requires stereo images, but high-resolution stereo images of Mars are rare. An alternative is the convolutional neural network (CNN) technique, which implicitly learns features by assigning corresponding inputs and outputs. In recent years, CNNs have exhibited promising performance in the 3D reconstruction of close-range scenes. In this paper, we present a CNN-based algorithm that is capable of generating DEMs from single images; the DEMs have the same resolutions as the input images. An existing low-resolution DEM is used to provide global information. Synthetic and real data, including context camera (CTX) images and DEMs from stereo High-Resolution Imaging Science Experiment (HiRISE) images, are used as training data. The performance of the proposed method is evaluated using single CTX images of representative landforms on Mars, and the generated DEMs are compared with those obtained from stereo HiRISE images. The experimental results show promising performance of the proposed method. The topographic details are well reconstructed, and the geometric accuracies achieve root-mean-square error (RMSE) values ranging from 2.1 m to 12.2 m (approximately 0.5 to 2 pixels in the image space). The experimental results show that the proposed CNN-based method has great potential for 3D surface reconstruction in planetary applications.

中文翻译：

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Mars3DNet：基于CNN的单幅图像对火星表面的高分辨率3D重建

三维（3D）表面模型，例如数字高程模型（DEM），对于行星探测任务和科学研究至关重要。当前火星表面的DEM主要是通过激光测高法或摄影测量法生成的，它们有各自的局限性。激光测高仪不能产生高分辨率的DEM。摄影测量需要立体图像，但是火星的高分辨率立体图像很少。卷积神经网络（CNN）技术是一种替代方法，它通过分配相应的输入和输出来隐式学习特征。近年来，CNN在近距离场景的3D重建中表现出令人鼓舞的性能。在本文中，我们提出了一种基于CNN的算法，该算法能够从单个图像生成DEM。DEM具有与输入图像相同的分辨率。现有的低分辨率DEM用于提供全局信息。包括上下文摄像机（CTX）图像和立体高分辨率成像科学实验（HiRISE）图像的DEM在内的合成和真实数据用作训练数据。使用火星上具有代表性的地貌的单个CTX图像评估所提出方法的性能，并将生成的DEM与从立体声HiRISE图像获得的DEM进行比较。实验结果表明该方法具有良好的性能。地形细节得到了很好的重建，并且几何精度达到了2.1 m到12.2 m的均方根误差（RMSE）值（图像空间中大约0.5到2个像素）。实验结果表明，基于CNN的方法在行星应用中具有3D表面重建的巨大潜力。