  Technology to recognize the type of component represented by a point cloud is
required in the reconstruction process of an as-built model of a process plant
based on laser scanning. The reconstruction process of a process plant through
laser scanning is divided into point cloud registration, point cloud
segmentation, and component type recognition and placement. Loss of shape data
or imbalance of point cloud density problems generally occur in the point cloud
data collected from large-scale facilities. In this study, we experimented with
the possibility of applying object recognition technology based on 3D deep
learning networks, which have been showing high performance recently, and
analyzed the results. For training data, we used a segmented point cloud
repository about components that we constructed by scanning a process plant.
For networks, we selected the multi-view convolutional neural network (MVCNN),
which is a view-based method, and PointNet, which is designed to allow the
direct input of point cloud data. In the case of the MVCNN, we also performed
an experiment on the generation method for two types of multi-view images that
can complement the shape occlusion of the segmented point cloud. In this
experiment, the MVCNN showed the highest retrieval accuracy of approximately
87%, whereas PointNet showed the highest retrieval mean average precision of
approximately 84%. Furthermore, both networks showed high recognition
performance for the segmented point cloud of plant components when there was
sufficient training data.
