AstroSplat: Physics-Based Gaussian Splatting for Rendering and Reconstruction of Small Celestial Bodies
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Abstract (arXiv)
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.
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References · 9
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
- 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,061
- 2023RoMa: Robust Dense Feature Matchingcited by 296
- 2024A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasetscited by 214
- 2024Gaussian Splatting for Efficient Satellite Image Photogrammetrycited by 15
- 2022AstroVision: Towards Autonomous Feature Detection and Description for Missions to Small Bodies Using Deep Learningcited by 10
- 2023Distributed Global Structure-from-Motion with a Deep Front-Endcited by 8
- 2025Stereophotoclinometry Revisitedcited by 1
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