# HyperGS: Hyperspectral 3D Gaussian Splatting Source: [HyperGS: Hyperspectral 3D Gaussian Splatting]() Collection index: [llms.txt]() Data updated: 2026-01-04T05:00:00.806Z Authors: Christopher Thirgood, Oscar Mendez, Erin Chao Ling, Jon Storey, Simon Hadfield Published: 2024-12-17T12:23:07.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Hyperspectral Novel View Synthesis on BaySpec](): Hyperspectral Novel View Synthesis; BaySpec; PSNR: 27.11. - [Hyperspectral Novel View Synthesis on Surface Optics (SOP) Datasets (test)](): Hyperspectral Novel View Synthesis; Surface Optics (SOP) Datasets (test); PSNR: 30.51. ## Original sources and implementation [Original paper]() [PDF]()