扩散去噪超光谱高斯点云
Diffusion-Denoised Hyperspectral Gaussian Splatting
AI总结：
本文提出DD-HGS方法，通过引入波长敏感的球谐函数、光谱损失和扩散去噪器，实现超光谱场景的3D显式重建，提升3D高斯点云方法的性能。
摘要：
超光谱成像（HSI）已被广泛用于农业应用中，用于非破坏性地估计植物营养成分和精确量化样品营养元素。最近，3D重建方法，如神经辐射场（NeRF），已被用于创建HSI场景的隐式神经表示。这种能力使能够在每个空间位置渲染超光谱通道组成，从而帮助在空间和光谱上定位目标物体的营养成分。然而，它在训练时间和渲染速度方面面临限制。在本文中，我们提出扩散去噪超光谱高斯点云（DD-HGS），通过引入对波长敏感的球谐函数、基于Kullback-Leibler散度的光谱损失以及基于扩散的去噪器，增强了最先进的3D高斯点云（3DGS）方法，以实现整个光谱范围内的3D显式重建。我们对Hyper-NeRF数据集上的多种真实世界超光谱场景进行了广泛的评估，以展示DD-HGS的有效性。结果表明，DD-HGS在所有先前发表的方法上实现了新的最先进性能。项目页面：https://dragonpg2000.github.io/DDHGS-website/
英文摘要：
Hyperspectral imaging (HSI) has been widely used in agricultural applications for non-destructive estimation of plant nutrient composition and precise quantification of sample nutritional elements. Recently, 3D reconstruction methods, such as Neural Radiance Field (NeRF), have been used to create implicit neural representations of HSI scenes. This capability enables the rendering of hyperspectral channel compositions at every spatial location, thereby helping localize the target object's nutrient composition both spatially and spectrally. 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 the hyperspectral scenes for the entire spectral range. We present extensive evaluations on diverse real-world hyperspectral scenes from the Hyper-NeRF dataset to show the effectiveness of our DD-HGS. The results demonstrate that DD-HGS achieves the new state-of-the-art performance compared to all the previously published methods. Project page: https://dragonpg2000.github.io/DDHGS-website/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
