3D EDGE DETECTION AND COMPARISON USING FOUR-CHANNEL IMAGES

Abstract. Point cloud segmentation, is a widespread field of research and it is useful in several research topics and applications such as 3D point cloud analysis, scene understanding, semantic segmentation etc. Architectural vector drawings constitute a valuable platform source for scientists and craftsmen while the production of such drawings is time-consuming because many of the creation steps are done manually. Detecting 3D edges in point clouds could provide useful information for the automation of the creation of 3D architectural vector drawings. Hence, a 3D edge detection method is proposed and evaluated with a proof-of-concept experiment and another one using a professional software. The scope of this effort is twofold, firstly the production of semantically enriched 3D dense point clouds exploiting four-channel images in order to detect 3D edges and secondly the comparison of the detected 3D edges with their corresponding edges in a textured 3D model. Comparing 3D edges in the early step of the 3D dense point cloud production and in the final step of 3D textured mesh, provides useful conclusions of the data used for the automatic creation of 3D drawings. Both of the experiments i.e., the proof-of-concept and using the professional SfM-MVS software were conducted using real world data of cultural heritage objects.