Distant-to-Close Novel View Synthesis for Asteroid Surface Imaging

Abstract

Predictively synthesizing high-quality, close-range asteroid surface views from distant optical remote sensing imagery is critical for mission planning and landing-site selection in asteroid exploration missions. However, distant observations inherently lack sufficient resolution and surface detail, limiting the existing novel view synthesis (NVS) methods. To address this, we introduce, to the best of our knowledge, the first framework for distant-to-close NVS, tailored for asteroid surface imaging. Our method features two key innovations. First, a 3-D Gaussian splatting (3D-GS) super-resolution (SR) module applies 2-D SR to generate high-resolution virtual close-range views from distant images, enriching the 3-D scene model with finer details. Second, an entropy-driven residual refinement strategy adaptively emphasizes structurally complex regions by assigning higher loss weights based on residual image entropy. This strategy triggers targeted subdivisions of 3-D Gaussians in the areas of high structural complexity. Experiments conducted on datasets from Hayabusa (Itokawa), Dawn (Vesta), Rosetta (67P/Churyumov-Gerasimenko), Hayabusa2 (Ryugu), and OSIRIS-REx (Bennu) missions demonstrate substantial improvements over baseline methods in quantitative metrics, such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS).