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。