The paper presents our efforts on CNN-based 3D reconstruction of the Martian surface using monocular images. The Viking colorized global mosaic and Mar Express HRSC blended DEM are used as training data. An encoder-decoder network system is employed in the framework. The encoder section extracts features from the images, which includes convolution layers and reduction layers. The decoder section consists of deconvolution layers and is to integrate features and convert the images to desired DEMs. In addition, skip connection between encoder and decoder section is applied, which offers more low-level features for the decoder section to improve its performance. Monocular Context Camera (CTX) images are used to test and verify the performance of the proposed CNN-based approach. Experimental results show promising performances of the proposed approach. Features in images are well utilized, and topographical details in images are successfully recovered in the DEMs. In most cases, the geometric accuracies of the generated DEMs are comparable to those generated by the traditional technology of photogrammetry using stereo images. The preliminary results show that the proposed CNN-based approach has great potential for 3D reconstruction of the Martian surface.
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机译：本文介绍了我们使用单眼图像的基于CNN的3D重建的基于CNN的3D重建。 Viking Colorized Global Mosaic和Mar Express HRSC混合DEM被用作培训数据。在框架中采用编码器解码器网络系统。编码器部分从图像中提取特征，该特征包括卷积层和还原层。解码器部分由解卷积层组成，并且是将特征集成并将图像转换为所需的DEM。此外，应用编码器和解码器部分之间的跳过连接，为解码器部分提供更多的低级功能，以提高其性能。单眼上下文相机（CTX）图像用于测试和验证所提出的基于CNN的方法的性能。实验结果表明了所提出的方法的有希望的表现。图像的功能很好地利用，图像中的地形细节在DEM中成功恢复。在大多数情况下，所生成的DEM的几何精度与使用立体图像的摄影测量方法产生的那些相当。初步结果表明，拟议的基于CNN的方法具有巨大的火星表面三维重建的潜力。
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