AstroSplat：基于物理的高斯散射用于小天体的渲染与重建
AstroSplat: Physics-Based Gaussian Splatting for Rendering and Reconstruction of Small Celestial Bodies
AI总结：
本文提出AstroSplat，一种基于物理的高斯散射框架，利用行星反射模型提升小天体表面的自主重建和光谱特性分析，通过NASA Dawn任务的真实图像验证了其在渲染性能和表面重建精度上的优势。
摘要：
基于图像的表面重建和特性分析对于小天体（如小行星）的任务至关重要，因为它影响任务规划、导航和科学分析。最近的高斯散射进展使神经场景表示更加逼真，但通常依赖于球谐函数强度参数化，这种参数化仅基于外观，不显式建模材料属性或光-表面相互作用。我们引入了AstroSplat，一种基于物理的高斯散射框架，整合了行星反射模型，以提高从原位影像中自主重建和光谱特性分析小天体表面的能力。所提出的框架在NASA Dawn任务的真实影像上进行了验证，展示了优于典型球谐函数参数化的渲染性能和表面重建精度。
英文摘要：
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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