Deep learning for 3D reconstruction of the martian surface using monocular images: A first glance

Z. Chen, B. Wu, W. C. Liu

Research output: Journal article publicationConference articleAcademic researchpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1111-1116
Number of pages6
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Issue numberB3
Publication statusPublished - 6 Aug 2020
Event2020 24th ISPRS Congress - Technical Commission III - Nice, Virtual, France
Duration: 31 Aug 20202 Sept 2020


  • Convolutional Neural Networks
  • Mars
  • Monocular Images
  • Surface Reconstruction

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development


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