SEGMENTATION OF BRIDGE POINT CLOUDS USING POINT CLOUD IMAGING AND DEEP LEARNING

Abstract

Point clouds are increasingly being gathered to improve the efficiency of inspecting infrastructures. In order to utilize the point clouds, segmentation is required to classify the point clouds by member. The purpose of this study was to develop a technique for automatic segmentation of bridge point clouds. 3D bridge point clouds were converted to 2D images, and then member estimation was performed by image-based semantic segmentation using Deeplabv3+, and the estimation results were reflected on the original 3D point clouds.