用扩散模型去噪提升高光谱3D重建精度与速度
Diffusion-Denoised Hyperspectral Gaussian Splatting
- 将高光谱信息融入3D高斯点云，实现波长感知建模
- 在Hyper-NeRF数据集上达到当前最优性能，渲染更快
- 适合农业营养检测、遥感分析等需要高精度光谱定位的场景
高光谱成像广泛应用于农业领域，用于非破坏性估算植物营养成分并精确量化样品中营养元素。近年来，如神经辐射场（NeRF）等3D重建方法被用于构建高光谱场景的隐式神经表示，可在任意空间位置渲染全波段光谱组成，从而实现目标物体营养成分在空间与光谱维度的精确定位。然而，该方法存在训练时间长、渲染速度慢的问题。本文提出扩散去噪高光谱高斯点云（DD-HGS），通过引入波长感知球谐函数、基于KL散度的光谱损失函数及扩散去噪器，增强现有3D高斯点云（3DGS）方法，实现全波段高光谱场景的显式3D重建。我们在Hyper-NeRF数据集上的多种真实世界高光谱场景中进行了充分评估，结果表明DD-HGS相较已有方法显著提升性能，达到当前最优水平。
原文摘要 · Abstract (English)
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/
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