  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.
