HyperGS: Hyperspectral 3D Gaussian Splatting
This addresses the problem of high-fidelity spatial and spectral rendering for hyperspectral imaging applications, representing a domain-specific advancement.
The paper tackles hyperspectral novel view synthesis by introducing HyperGS, a framework based on latent 3D Gaussian splatting that encodes material properties from multi-view hyperspectral data, achieving a 14dB accuracy improvement over prior models while offering faster rendering.
We introduce HyperGS, a novel framework for Hyperspectral Novel View Synthesis (HNVS), based on a new latent 3D Gaussian Splatting (3DGS) technique. Our approach enables simultaneous spatial and spectral renderings by encoding material properties from multi-view 3D hyperspectral datasets. HyperGS reconstructs high-fidelity views from arbitrary perspectives with improved accuracy and speed, outperforming currently existing methods. To address the challenges of high-dimensional data, we perform view synthesis in a learned latent space, incorporating a pixel-wise adaptive density function and a pruning technique for increased training stability and efficiency. Additionally, we introduce the first HNVS benchmark, implementing a number of new baselines based on recent SOTA RGB-NVS techniques, alongside the small number of prior works on HNVS. We demonstrate HyperGS's robustness through extensive evaluation of real and simulated hyperspectral scenes with a 14db accuracy improvement upon previously published models.