  As Neural Radiance Field (NeRF) implementations become faster, more efficient
and accurate, their applicability to real world mapping tasks becomes more
accessible. Traditionally, 3D mapping, or scene reconstruction, has relied on
expensive LiDAR sensing. Photogrammetry can perform image-based 3D
reconstruction but is computationally expensive and requires extremely dense
image representation to recover complex geometry and photorealism. NeRFs
perform 3D scene reconstruction by training a neural network on sparse image
and pose data, achieving superior results to photogrammetry with less input
data. This paper presents an evaluation of two NeRF scene reconstructions for
the purpose of estimating the diameter of a vertical PVC cylinder. One of these
are trained on commodity iPhone data and the other is trained on robot-sourced
imagery and poses. This neural-geometry is compared to state-of-the-art
lidar-inertial SLAM in terms of scene noise and metric-accuracy.
