  This paper devises, implements and benchmarks a novel shape retrieval method
that can accurately match individual labelled point clusters (instances) of
existing industrial facilities with their respective CAD models. It employs a
combination of image and point cloud deep learning networks to classify and
match instances to their geometrically similar CAD model. It extends our
previous research on geometric digital twin generation from point cloud data,
which currently is a tedious, manual process. Experiments with our joint
network reveal that it can reliably retrieve CAD models at 85.2\% accuracy. The
proposed research is a fundamental framework to enable the geometric Digital
Twin (gDT) pipeline and incorporate the real geometric configuration into the
Digital Twin.
