HyperGS: Hyperspectral 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2412.12849v1
• Date: Tue, 17 Dec 2024 12:23:07 GMT
• Title: HyperGS: Hyperspectral 3D Gaussian Splatting
• Authors: Christopher Thirgood, Oscar Mendez, Erin Chao Ling, Jon Storey, Simon Hadfield,
• Abstract summary: We introduce HyperGS, a novel framework for Hyperspectral Novel View Synthesis (HNVS)<n>Our approach enables simultaneous spatial and spectral renderings by encoding material properties from multi-view 3D hyperspectral datasets.<n>We demonstrate HyperGS's robustness through extensive evaluation of real and simulated hyperspectral scenes with a 14db accuracy improvement upon previously published models.
• Score: 13.07553815605148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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