ABSTRACT:As a country with 11 properties included on the World Heritage List and approximately 12,000 important cultural properties, Korea has been continuously carrying out the inventory and documentation of cultural properties to conserve and manage them since the 1960s. The inventory of cultural properties had been carried out by making and managing a register which recorded basic information mainly on state-designated cultural properties such as their size, quantity, and location. The documentation of cultural properties was also carried out by making measured drawings. However, the inventory and documentation done under the previous analog method had a limit to the information it could provide for the effective conservation and management of cultural prope rties. Moreover, in recent times important cultural properties have frequently been damaged by man-made and natural disasters such as arson, forest fires, and floods, so an alternative was required.Accordingly, Korea actively introduced digital techniques led by the government for the inventory and documentation of important cultural properties. In this process, the government established the concept of a digital set, built a more efficie nt integrated data management system, and created standardized guidelines to maximize the effectiveness of data acquisition, management, and utilization that greatly increased the level of digital inventory, documentation, and archiving.
The success of deep learning technology depends on the availability of adequate amounts of data for training deep neural networks. Many repositories of general two-(2D) and three-dimensional (3D) data are available, but relatively few repositories of 3D models from the engineering field exist. In industrial process plants, the 3D shapes of plants are captured accurately by creating point clouds through laser scanning. To develop 3D deep learning models that employ point clouds for process plants, it is necessary to first generate the point cloud data required to train deep neural networks for each constituent part (plant item) of a process plant. This study describes the results of an attempt to construct a segmented point cloud repository of the various pipework sections and fittings that constitute a process plant for use in deep learning applications.
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