扩散去噪高光谱高斯溅射 (Diffusion Denoised Hyperspectral Gaussian Splatting)

Hyperspectral imaging (HSI) has been widely used in agricultural applications for non-destructive estimation of plant nutrient composition and precise determination of nutritional elements of samples. Recently, 3D reconstruction methods have been used to create implicit neural representations of HSI scenes, which can help localize the target object's nutrient composition spatially and spectrally. Neural Radiance Field (NeRF) is a cutting-edge implicit representation that can be used to render hyperspectral channel compositions of each spatial location from any viewing direction. However, it faces limitations in training time and rendering speed. In this paper, we propose Diffusion-Denoised Hyperspectral Gaussian Splatting (DD-HGS), which enhances the state-of-the-art 3D Gaussian Splatting (3DGS) method with wavelength-aware spherical harmonics, a Kullback-Leibler divergence-based spectral loss, and a diffusion-based denoiser to enable 3D explicit reconstruction of hyperspectral scenes across the full spectral range. We present extensive evaluations on diverse real-world hyperspectral scenes from the Hyper-NeRF dataset to show the effectiveness of DD-HGS. The results demonstrate that DD-HGS achieves new state-of-the-art performance among previously published methods. Project page: https://dragonpg2000.github.io/DDHGS-website/

翻译：高光谱成像（HSI）已广泛应用于农业应用中，用于无损估计植物营养成分并精确测定样品的营养元素。近年来，三维重建方法被用于创建HSI场景的隐式神经表示，这有助于在空间和光谱上定位目标对象的营养成分。神经辐射场（NeRF）是一种先进的隐式表示方法，可从任意视角渲染每个空间位置的高光谱通道组成。然而，它在训练时间和渲染速度方面存在局限。本文提出扩散去噪高光谱高斯溅射（DD-HGS），该方法通过波长感知球谐函数、基于Kullback-Leibler散度的光谱损失和基于扩散的去噪器，增强了最先进的三维高斯溅射（3DGS）方法，实现了跨全光谱范围的高光谱场景三维显式重建。我们在Hyper-NeRF数据集中的多样化真实世界高光谱场景上进行了广泛评估，以展示DD-HGS的有效性。结果表明，DD-HGS在已发表方法中达到了新的最先进性能。项目页面：https://dragonpg2000.github.io/DDHGS-website/