Point-Cloud Segmentation for 3D Edge Detection and Vectorization

Abstract

The creation of 2D–3D architectural vector drawings constitutes a manual, labor-intensive process. The scientific community has not provided an automated approach for the production of 2D–3D architectural drawings of cultural-heritage objects yet, regardless of the undoubtable need of many scientific fields. This paper presents an automated method which addresses the problem of detecting 3D edges in point clouds by leveraging a set of RGB images and their 2D edge maps. More concretely, once the 2D edge maps have been produced exploiting manual, semi-automated or automated methods, the RGB images are enriched with an extra channel containing the edge semantic information corresponding to each RGB image. The four-channel images are fed into a Structure from Motion–Multi View Stereo (SfM-MVS) software and a semantically enriched dense point cloud is produced. Then, using the semantically enriched dense point cloud, the points belonging to a 3D edge are isolated from all the others based on their label value. The detected 3D edge points are decomposed into set of points belonging to each edge and fed into the 3D vectorization procedure. Finally, the 3D vectors are saved into a “.dxf” file. The previously described steps constitute the 3DPlan software, which is available on GitHub. The efficiency of the proposed software was evaluated on real-world data of cultural-heritage assets.

Keywords

• edge detection
• point-cloud segmentation
• photogrammetry
• 3D computer vision
• cultural heritage
• SfM-MVS