@article{heritage8030091,

title = {Detecting Three-Dimensional Straight Edges in Point Clouds Based on Normal Vectors},

author = {Antonia Makka and Maria Pateraki and Thodoris Betsas and Andreas Georgopoulos},

url = {https://www.mdpi.com/2571-9408/8/3/91},

doi = {10.3390/heritage8030091},

issn = {2571-9408},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Heritage},

volume = {8},

number = {3},

abstract = {Edge detection is essential for numerous applications in various engineering and scientific fields, including photogrammetry and computer vision. Edge detection can be applied to a variety of 2D and 3D data types, enabling tasks like feature extraction, object recognition and scene reconstruction. Currently, 2D edge detection in image data can achieve high accuracy through various automated methods. At the same time, edge detection in 3D space remains a challenge due to the computational demands and parameterization of existing algorithms. However, with the growing volume of data, the need for automated edge extraction that delivers high accuracy and reliable performance across diverse datasets has become more critical than ever. In this context, we propose an algorithm that implements a direct method for automated 3D edge detection in point clouds. The suggested method significantly aids architects by automating the extraction of 3D vectors, a process that is traditionally time-consuming and labor-intensive when performed manually. The proposed algorithm performs edge detection through a five-stage pipeline. More specifically, it utilizes the differences in the direction of normal vectors to identify finite edges. These edges are afterwards refined and grouped into segments which are then fitted to highlight the presence of 3D edges. The proposed approach was tested on both simulated and real-world data with promising results in terms of accuracy. For the synthetic data, the proposed method managed to detect 92% of the straight edges for the higher density meshes.},

keywords = {3D mesh, Edge detection, graph theory, least squares, Point Cloud, RANSAC},

pubstate = {published},

tppubtype = {article}

}

@article{isprs-archives-XLVIII-2-W4-2024-295-2024,

title = {3D EDGE DETECTION BASED ON NORMAL VECTORS},

author = {A. Makka and M. Pateraki and T. Betsas and A. Georgopoulos},

url = {https://isprs-archives.copernicus.org/articles/XLVIII-2-W4-2024/295/2024/},

doi = {10.5194/isprs-archives-XLVIII-2-W4-2024-295-2024},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},

volume = {XLVIII-2/W4-2024},

pages = {295\textendash300},

abstract = {Edge detection is supported by extensive research and is part of different photogrammetric and computer vision tasks across numerous application areas. While 2D edge detection may achieve high accuracy results from several automated methods, the automation of edge detection in 3D space remains a challenge. Existing methods are often computationally demanding and heavily parameterized, leading to a lack of adaptability. In real-world applications 3D edges, representing the object boundaries and break lines, are crucial, particularly in fields such as computer vision, robotics and architecture. In this context, we present a method that automates 3D edge detection in 3D point clouds exploiting the normal vectors’ direction differences to detect finite edges, which are further pruned and grouped to edge segments and fitted to indicate the presence of a 3D edge.},

keywords = {3D mesh, Edge detection, graph theory, least squares, Point Cloud, RANSAC},

pubstate = {published},

tppubtype = {article}

}

@article{betsas_point-cloud_2022,

title = {Point-Cloud Segmentation for 3D Edge Detection and Vectorization},

author = {Thodoris Betsas and Andreas Georgopoulos},

url = {https://www.mdpi.com/2571-9408/5/4/208},

doi = {10.3390/heritage5040208},

issn = {2571-9408},

year  = {2022},

date = {2022-12-09},

journal = {Heritage},

volume = {5},

number = {4},

pages = {4037--4060},

abstract = {The creation of 2D\textendash3D architectural vector drawings constitutes a manual, labor-intensive process. The scientific community has not provided an automated approach for the production of 2D\textendash3D 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\textendashMulti 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.},

note = {Number: 4 

Publisher: Multidisciplinary Digital Publishing Institute},

keywords = {3D computer vision, Cultural heritage, Edge detection, Photogrammetry, point-cloud segmentation, SfM-MVS},

pubstate = {published},

tppubtype = {article}

}

@article{Dolapsaki2021,

title = {Edge Detection in 3D Point Clouds Using Digital Images},

author = {Maria Melina Dolapsaki and Andreas Georgopoulos},

url = {https://doi.org/10.3390/ijgi10040229},

doi = {10.3390/ijgi10040229},

year  = {2021},

date = {2021-04-01},

journal = {ISPRS Int. J. Geo-Information},

volume = {10},

number = {4},

pages = {229},

publisher = {MDPI AG},

abstract = {This paper presents an effective and semi-automated method for detecting 3D edges in 3D point clouds with the help of high-resolution digital images. The effort aims to contribute towards addressing the unsolved problem of automated production of vector drawings from 3D point clouds of cultural heritage objects. Edges are the simplest primitives to detect in an unorganized point cloud and an algorithm was developed to perform this task. The provided edges are defined and measured on 2D digital images of known orientation, and the algorithm determines the plane defined by the edge on the image and its perspective center. This is accomplished by applying suitable transformations to the image coordinates of the edge points based on the Analytical Geometry relationships and properties of planes in 3D space. This plane inevitably contains the 3D points of the edge in the point cloud. The algorithm then detects and isolates those points which define the edge in the world system. Finally, the goal is to reliably locate the points that describe the desired edge in their true position in the geodetic space, using several constraints. The algorithm is firstly investigated theoretically for its efficiency using simulation data and then assessed under real conditions and under different image orientations and lengths of the edge on the image. The results are presented and evaluated.},

keywords = {Cultural heritage, Edge detection, large scales, Point clouds},

pubstate = {published},

tppubtype = {article}

}