Hyperspectral Imagery (HSI) has been used in many applications to non-destructively determine the material and/or chemical compositions of samples. There is growing interest in creating 3D hyperspectral reconstructions, which could provide both spatial and spectral information while also mitigating common HSI challenges such as non-Lambertian surfaces and translucent objects. However, traditional 3D reconstruction with HSI is difficult due to technological limitations of hyperspectral cameras. In recent years, Neural Radiance Fields (NeRFs) have seen widespread success in creating high quality volumetric 3D representations of scenes captured by a variety of camera models. Leveraging recent advances in NeRFs, we propose computing a hyperspectral 3D reconstruction in which every point in space and view direction is characterized by wavelength-dependent radiance and transmittance spectra. To evaluate our approach, a dataset containing nearly 2000 hyperspectral images across 8 scenes and 2 cameras was collected. We perform comparisons against traditional RGB NeRF baselines and apply ablation testing with alternative spectra representations. Finally, we demonstrate the potential of hyperspectral NeRFs for hyperspectral super-resolution and imaging sensor simulation. We show that our hyperspectral NeRF approach enables creating fast, accurate volumetric 3D hyperspectral scenes and enables several new applications and areas for future study.

利用NeRFs的最新进展，我们提出了一种计算超光谱3D重建的方法，其中空间中的每个点和视角都以波长相关辐射和透射光谱为特征。通过对近2000个超光谱图像进行对比和消融测试，我们证明了超光谱NeRF的潜力，包括超分辨率和成像传感器模拟。我们展示了超光谱NeRF方法可以快速、准确地创建体积化的3D超光谱场景，并为未来研究提供了几个新的应用和领域。

高光谱神经辐射场