AstroSplat: Physics-Based Gaussian Splatting for Rendering and Reconstruction of Small Celestial Bodies
This work addresses the need for improved autonomous reconstruction and photometric characterization of small-body surfaces for space missions, representing an incremental advance by integrating physics into an existing neural representation method.
The paper tackled the problem of image-based surface reconstruction and characterization for small celestial bodies by introducing AstroSplat, a physics-based Gaussian splatting framework that integrates planetary reflectance models, resulting in superior rendering performance and surface reconstruction accuracy on real NASA Dawn mission imagery.
Image-based surface reconstruction and characterization are crucial for missions to small celestial bodies (e.g., asteroids), as it informs mission planning, navigation, and scientific analysis. Recent advances in Gaussian splatting enable high-fidelity neural scene representations but typically rely on a spherical harmonic intensity parameterization that is strictly appearance-based and does not explicitly model material properties or light-surface interactions. We introduce AstroSplat, a physics-based Gaussian splatting framework that integrates planetary reflectance models to improve the autonomous reconstruction and photometric characterization of small-body surfaces from in-situ imagery. The proposed framework is validated on real imagery taken by NASA's Dawn mission, where we demonstrate superior rendering performance and surface reconstruction accuracy compared to the typical spherical harmonic parameterization.