@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{Agrafiotis2019a,

title = {DepthLearn: Learning to correct the refraction on point clouds derived from aerial imagery for accurate dense shallow water bathymetry based on SVMs-fusion with LiDAR point clouds},

author = {Panagiotis Agrafiotis and Dimitrios Skarlatos and Andreas Georgopoulos and Konstantinos Karantzalos},

url = {https://www.mdpi.com/2072-4292/11/19/2225},

doi = {10.3390/rs11192225},

issn = {20724292},

year  = {2019},

date = {2019-09-01},

journal = {Remote Sens.},

volume = {11},

number = {19},

pages = {2225},

publisher = {MDPI AG},

abstract = {The determination of accurate bathymetric information is a key element for near offshore activities; hydrological studies, such as coastal engineering applications, sedimentary processes, hydrographic surveying, archaeological mapping and biological research. Through structure from motion (SfM) and multi-view-stereo (MVS) techniques, aerial imagery can provide a low-cost alternative compared to bathymetric LiDAR (Light Detection and Ranging) surveys, as it offers additional important visual information and higher spatial resolution. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this article, in order to overcome the water refraction errors in a massive and accurate way, we employ machine learning tools, which are able to learn the systematic underestimation of the estimated depths. In particular, an SVR (support vector regression) model was developed, based on known depth observations from bathymetric LiDAR surveys, which is able to accurately recover bathymetry from point clouds derived from SfM-MVS procedures. Experimental results and validation were based on datasets derived from different test-sites, and demonstrated the high potential of our approach. Moreover, we exploited the fusion of LiDAR and image-based point clouds towards addressing challenges of both modalities in problematic areas.},

keywords = {Aerial imagery, Bathymetry, Data integration, Fusion, LiDAR, Machine Learning, Point Cloud, Refraction effect, Seabed Mapping, SVM, UAV},

pubstate = {published},

tppubtype = {article}

}

@article{Agrafiotis2019,

title = {SHALLOW WATER BATHYMETRY MAPPING from UAV IMAGERY BASED on MACHINE LEARNING},

author = {P Agrafiotis and D Skarlatos and A Georgopoulos and K Karantzalos},

url = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W10/9/2019/},

doi = {10.5194/isprs-archives-XLII-2-W10-9-2019},

issn = {21949050},

year  = {2019},

date = {2019-04-01},

journal = {ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci.},

volume = {42},

number = {2/W10},

pages = {9--16},

abstract = {The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths. In the proposed approach, based on known depth observations from bathymetric LiDAR surveys, an SVR model was developed able to estimate more accurately the real depths of point clouds derived from SfM-MVS procedures. Experimental results over two test sites along with the performed quantitative validation indicated the high potential of the developed approach.},

keywords = {Bathymetry, Machine Learning, Point Cloud, Refraction effect, Seabed Mapping, SVM, UAV},

pubstate = {published},

tppubtype = {article}

}

@article{Maltezos2018,

title = {Plane detection of polyhedral cultural heritage monuments: The case of tower of winds in Athens},

author = {Evangelos Maltezos and Charalabos Ioannidis},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2352409X17305126},

doi = {10.1016/j.jasrep.2018.03.036},

issn = {2352409X},

year  = {2018},

date = {2018-06-01},

journal = {J. Archaeol. Sci. Reports},

volume = {19},

pages = {562--574},

publisher = {Elsevier Ltd},

abstract = {This study introduces an efficient and easy to implement plane detection approach towards the extraction of high-level information from 3D point clouds associated with polyhedral cultural heritage monuments. An adapted version of the randomized Hough transform (RHT) called “adaptive point randomized Hough transform” (APRHT) and a multiscale framework in terms of Level of Detail 1 (LoD 1) and LoD 2 are proposed. A dense image matching point cloud of an octagonal tower called Tower of Winds, which is situated on the northern foot of the Acropolis hill in Athens was used. A pre-process is carried out to extract points associated with the vertical structural elements. Then a plane detection process is performed in terms of LoD 1 to calculate the plane parameters ($theta$, $phi$ and $rho$) of each of the eight planar surfaces using a coarse form of the entire monument, that is, a sparse point cloud extracted via subsampling process. A mask of one representative detected planar surface is used to clip the initial point cloud with the initial point density. Then, a second plane detection process in terms of LoD 2 at the clipped point cloud is implemented to calculate the corresponding accurate plane parameters. The results are useful for cultural heritage preservation purposes and illustrate the robustness, efficiency and the rapidity of the proposed framework.},

keywords = {Dense image matching, Plane detection, Point Cloud, Randomized Hough transform},

pubstate = {published},

tppubtype = {article}

}