ESGS: A 3D Reconstruction Method for the Martian Surface Based on Optical Remote Sensing Images

Abstract

Mars exploration is an advanced field of global deep space exploration. Accurate three-dimensional reconstruction of the Martian surface topography is very important for autonomous navigation, scientific target recognition, and operation planning. In order to meet the analysis requirements of the Martian surface scene, this paper proposes an explicit surface-geometry-constrained Gaussian splatting (ESGS) method. Firstly, this method includes a normal and depth prior estimation network (NDN) that generates normal and depth priors from Martian surface image data, thereby promoting the fusion of semantic and multi-view contextual information to enhance the geometric accuracy of 3D reconstruction of the Martian surface. Secondly, we designed the Gaussian parameter-based deformable fusion network (GPDFN) to fuse multi-receptive-field feature information. Finally, we collected Martian surface remote sensing images from NASA, constructed a Martian surface 3D reconstruction dataset named Mars_3D using the COLMAP method, annotated depth and normal labels for its seven real-world scenes and two Blender-generated scenes, and conducted comparative experiments with eight excellent algorithms on this dataset to validate the effectiveness of our method in 3D reconstruction of the Martian surface using remote sensing images. Experiments show that the average SSIM of the ESGS method in this article is 0.6946, PSNR is 23.40 dB, and LPIPS is 0.253 on the Mars_3D dataset, demonstrating superior overall performance compared to all other models and enhancing the quality of 3D reconstruction of the Martian surface.