  Neural Radiance Fields (NeRFs) are trained using a set of camera poses and
associated images as input to estimate density and color values for each
position. The position-dependent density learning is of particular interest for
photogrammetry, enabling 3D reconstruction by querying and filtering the NeRF
coordinate system based on the object density. While traditional methods like
Structure from Motion are commonly used for camera pose calculation in
pre-processing for NeRFs, the HoloLens offers an interesting interface for
extracting the required input data directly. We present a workflow for
high-resolution 3D reconstructions almost directly from HoloLens data using
NeRFs. Thereby, different investigations are considered: Internal camera poses
from the HoloLens trajectory via a server application, and external camera
poses from Structure from Motion, both with an enhanced variant applied through
pose refinement. Results show that the internal camera poses lead to NeRF
convergence with a PSNR of 25\,dB with a simple rotation around the x-axis and
enable a 3D reconstruction. Pose refinement enables comparable quality compared
to external camera poses, resulting in improved training process with a PSNR of
27\,dB and a better 3D reconstruction. Overall, NeRF reconstructions outperform
the conventional photogrammetric dense reconstruction using Multi-View Stereo
in terms of completeness and level of detail.
