SB-HyperGS: Spectral–Spatial Representation Capacity Reallocation for Sparse Hyperspectral Gaussian Splatting

Hyperspectral 3D reconstruction aims to recover dense spectral radiance fields from sparse spectral observations. However, directly extending 3D Gaussian Splatting typically requires assigning an independent spectral representation to each Gaussian primitive, resulting in substantial spectral parameter redundancy and limiting spatial representation capacity under a finite parameter budget. We propose a material-efficient hyperspectral Gaussian Splatting framework that replaces independent per-Gaussian spectral storage with a compact material representation. By learning shared material spectral bases and Gaussian-specific local mixing coefficients, our method reduces redundant spectral parameters and reallocates the saved representation capacity to spatial primitives. We further introduce material-aware optimization and adaptive spatial refinement to improve reconstruction from sparse spectral observations. Experiments on multi-scene hyperspectral benchmarks show that our method achieves a better quality–parameter trade-off than direct hyperspectral Gaussian Splatting. Under matched parameter budgets, our method improves reconstruction quality while substantially reducing per-primitive spectral representation overhead, demonstrating the effectiveness of compact material representations for scalable hyperspectral neural rendering.