用新方法实现高光谱3D视角合成，速度快精度高。
HyperGS: Hyperspectral 3D Gaussian Splatting
- 在学习的潜在空间中进行高维数据渲染，提升效率与稳定性。
- 真实与模拟场景下比之前模型提升14db精度，重建质量高。
- 首次构建高光谱新视角合成基准，适合遥感与三维成像研究者。
我们提出HyperGS，一种基于新型潜在3D高斯点阵（3DGS）技术的高光谱新视角合成（HNVS）框架。该方法通过多视角3D高光谱数据编码材料属性，实现空间与光谱信息的同时渲染。HyperGS能从任意视角重建高保真图像，准确率与速度均优于现有方法。针对高维数据挑战，模型在学习的潜在空间中进行视角合成，引入像素级自适应密度函数与剪枝技术，提升训练稳定性和效率。此外，我们构建首个HNVS基准，集成多个基于最新RGB-NVS技术的基线模型，并包含少量先前高光谱视角合成工作。通过大量真实与模拟场景评估，HyperGS相较已有模型实现14db精度提升，展现出强鲁棒性。
原文摘要 · Abstract (English)
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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