@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{rs13061053,

title = {Semantically Derived Geometric Constraints for MVS Reconstruction of Textureless Areas},

author = {Elisavet Konstantina Stathopoulou and Roberto Battisti and Dan Cernea and Fabio Remondino and Andreas Georgopoulos},

url = {https://www.mdpi.com/2072-4292/13/6/1053},

doi = {10.3390/rs13061053},

issn = {2072-4292},

year  = {2021},

date = {2021-03-10},

urldate = {2021-03-10},

journal = {Remote Sensing},

volume = {13},

number = {6},

abstract = {Conventional multi-view stereo (MVS) approaches based on photo-consistency measures are generally robust, yet often fail in calculating valid depth pixel estimates in low textured areas of the scene. In this study, a novel approach is proposed to tackle this challenge by leveraging semantic priors into a PatchMatch-based MVS in order to increase confidence and support depth and normal map estimation. Semantic class labels on image pixels are used to impose class-specific geometric constraints during multiview stereo, optimising the depth estimation on weakly supported, textureless areas, commonly present in urban scenarios of building facades, indoor scenes, or aerial datasets. Detecting dominant shapes, e.g., planes, with RANSAC, an adjusted cost function is introduced that combines and weighs both photometric and semantic scores propagating, thus, more accurate depth estimates. Being adaptive, it fills in apparent information gaps and smoothing local roughness in problematic regions while at the same time preserves important details. Experiments on benchmark and custom datasets demonstrate the effectiveness of the presented approach.},

keywords = {3D reconstruction, dense point cloud, depth estimation, multi view stereo (MVS), PatchMatch, Plane detection, RANSAC, semantic segmentation},

pubstate = {published},

tppubtype = {article}

}