  Liquid Argon Time Projection Chambers (LArTPC) are particle imaging detectors
recording 2D or 3D images of trajectories of charged particles. Identifying
points of interest in these images, namely the initial and terminal points of
track-like particle trajectories such as muons and protons, and the initial
points of electromagnetic shower-like particle trajectories such as electrons
and gamma rays, is a crucial step of identifying and analyzing these particles
and impacts the inference of physics signals such as neutrino interaction. The
Point Proposal Network is designed to discover these specific points of
interest. The algorithm predicts with a sub-voxel precision their spatial
location, and also determines the category of the identified points of
interest. Using as a benchmark the PILArNet public LArTPC data sample in which
the voxel resolution is 3mm/voxel, our algorithm successfully predicted 96.8%
and 97.8% of 3D points within a distance of 3 and 10~voxels from the provided
true point locations respectively. For the predicted 3D points within 3 voxels
of the closest true point locations, the median distance is found to be 0.25
voxels, achieving the sub-voxel level precision. In addition, we report our
analysis of the mistakes where our algorithm prediction differs from the
provided true point positions by more than 10~voxels. Among 50 mistakes
visually scanned, 25 were due to the definition of true position location, 15
were legitimate mistakes where a physicist cannot visually disagree with the
algorithm's prediction, and 10 were genuine mistakes that we wish to improve in
the future. Further, using these predicted points, we demonstrate a simple
algorithm to cluster 3D voxels into individual track-like particle trajectories
with a clustering efficiency, purity, and Adjusted Rand Index of 96%, 93%, and
91% respectively.
