High-Fidelity Local 3D Terrain Reconstruction for Asteroids via Generative Modeling

Asteroid exploration, sample return, and defense missions critically require accurate three-dimensional (3D) reconstructions of target celestial bodies, including global shape models and high-fidelity local topographic details (Richardson et al. 2022; Thomas et al. 2023). Traditional methods, such as photogrammetry, stereophotoclinometry, and shape-from-shading have been extensively used to derive global and local 3D models from images (Gaskell et al. 2008; Palmer et al. 2022)). However, achieving reliable geometric accuracy and capturing fine-scale terrain features from limited observational data—such as sparse viewpoints and constrained illumination—remains a significant challenge.

This paper focuses on local topographic refinement, a process crucial for various advanced applications. It supports tasks such as high-precision engineering and scientific site reconstruction, provides high-detail maplets enabling more accurate global 3D reconstructions, and offers high-fidelity topographic features essential for precision Terrain Relative Navigation (TRN) (Gaskell et al. 2008, 2023; Olds et al. 2015).

The core innovation of the proposed method is the ability to achieve detailed topographic reconstruction of a local surface region using only a limited number of images—potentially even a single image—under constrained viewing and illumination conditions. To address the ill-posed problem of recovering high-frequency topographic details from sparse or limited observational data, we propose leveraging powerful diffusion priors from generative models pre-trained on low-resolution images and digital elevation models (DEMs). These priors encode geometric knowledge that can guide the reconstruction process, thereby significantly reducing the stringent requirement for extensive multi-view, multi-illumination image sets. Furthermore, the proposed generative framework is designed to inherently incorporate principles similar to GeoWizard’s geometry switcher and cross-domain attention mechanisms (Fu et al. 2024). This design facilitates a joint estimation of both elevation and implied surface orientation (normals). This joint estimation aims to ensure high geometric consistency between the derived elevation map and its corresponding surface normals, ultimately yielding a more reliable and geometrically accurate high-resolution topography.

Experimental validation was conducted on real data of NASA's OSIRIS-REx Mission to Asteroid Bennu, including MapCam, PolyCam, and NavCam images. Starting with 75 cm resolution local DEMs (99×99 grids), high-resolution DEMs were iteratively generated at 25 cm, 18 cm, and 10 cm resolutions. This process involved refining the DEM from the preceding resolution level using just 1-3 corresponding high-resolution images for each successive target resolution. The DEM with spatial resolution of 10 cm achieves less than 1 cm average root mean square error (RMSE) compared to NASA published 5 cm ground truth. Additionally, to evaluate the effectiveness of the refined DEM, we rendered it using the same illumination and observation conditions as the captured image. Scale-Invariant Feature Transform (SIFT) was then applied to match the rendered image against the captured image. The high matching success rate indicates the reconstructed terrain captures effective image textures, which further validates the reliability of our method in capturing terrain details. The refined DEM also exhibit enhanced compatibility with TRN task. The landmark matching success rate exceeds 95%, which is significantly higher than the rates achieved without terrain detail.

Reference

Fu, X., Yin, W., Hu, M., et al. (2024). Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image. In European Conference on Computer Vision Conference (pp. 241-258)

Gaskell, R. W., Barnouin‐Jha, O. S., Scheeres, D. J., et al. (2008). Characterizing and navigating small bodies with imaging data. Meteoritics & Planetary Science, 43(6), 1049-1061.

Gaskell, R. W., Barnouin, O. S., Daly, et al. (2023). Stereophotoclinometry on the OSIRIS-REx mission: mathematics and methods. The Planetary Science Journal, 4(4), 63.

Palmer, E. E., Gaskell, R., Daly, M. G., et al. (2022). Practical stereo-photoclinometry for modeling shape and topography on planetary missions. The Planetary Science Journal, 3(5), 102.

Richardson, D. C., Agrusa, H. F., Barbee, B., et al. (2022). Predictions for the dynamical states of the Didymos system before and after the planned DART impact. The planetary science journal, 3(7), 157.

Olds, R., May, A., Mario, C., et al. (2015). The application of optical based feature tracking to OSIRIS-REx asteroid sample collection. In AAS Guidance, Navigation, & Control Conference, AAS pp. 15-124.

Thomas, C. A., Naidu, S. P., Scheirich, P., et al. (2023). Orbital period change of Dimorphos due to the DART kinetic impact. Nature, 616(7957), 448-451.