  Neural Radiance Fields (NeRFs) have become a rapidly growing research field
with the potential to revolutionize typical photogrammetric workflows, such as
those used for 3D scene reconstruction. As input, NeRFs require multi-view
images with corresponding camera poses as well as the interior orientation. In
the typical NeRF workflow, the camera poses and the interior orientation are
estimated in advance with Structure from Motion (SfM). But the quality of the
resulting novel views, which depends on different parameters such as the number
and distribution of available images, as well as the accuracy of the related
camera poses and interior orientation, is difficult to predict. In addition,
SfM is a time-consuming pre-processing step, and its quality strongly depends
on the image content. Furthermore, the undefined scaling factor of SfM hinders
subsequent steps in which metric information is required. In this paper, we
evaluate the potential of NeRFs for industrial robot applications. We propose
an alternative to SfM pre-processing: we capture the input images with a
calibrated camera that is attached to the end effector of an industrial robot
and determine accurate camera poses with metric scale based on the robot
kinematics. We then investigate the quality of the novel views by comparing
them to ground truth, and by computing an internal quality measure based on
ensemble methods. For evaluation purposes, we acquire multiple datasets that
pose challenges for reconstruction typical of industrial applications, like
reflective objects, poor texture, and fine structures. We show that the
robot-based pose determination reaches similar accuracy as SfM in non-demanding
cases, while having clear advantages in more challenging scenarios. Finally, we
present first results of applying the ensemble method to estimate the quality
of the synthetic novel view in the absence of a ground truth.
