@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{betsas_3d_2022,

title = {3D EDGE DETECTION AND COMPARISON USING FOUR-CHANNEL IMAGES},

author = {T. Betsas and A. Georgopoulos},

url = {https://isprs-archives.copernicus.org/articles/XLVIII-2-W2-2022/9/2022/},

doi = {10.5194/isprs-archives-XLVIII-2-W2-2022-9-2022},

issn = {2194-9034},

year  = {2022},

date = {2022-12-08},

urldate = {2022-12-08},

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

volume = {XLVIII-2/W2-2022},

pages = {9--15},

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.},

keywords = {3D Edge Comparison, 3D Edge Detection, Point Cloud Segmentation, SfM-MVS},

pubstate = {published},

tppubtype = {article}

}

@article{Kontogianni2017b,

title = {INVESTIGATING THE EFFECT OF FOCUS STACKING ON SFM-MVS ALGORITHMS},

author = {Georgia Kontogianni and Regina Chliverou and A Koutsoudis and G Pavlidis and Andreas Georgopoulos},

url = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W3/385/2017/},

doi = {10.5194/isprs-archives-XLII-2-W3-385-2017},

issn = {2194-9034},

year  = {2017},

date = {2017-02-01},

journal = {ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.},

volume = {XLII-2/W3},

pages = {385--389},

abstract = {The Depth of Field (DoF) is a vital factor in photogrammetric applications. Its effect is in most cases pretty obvious especially when capturing small artefacts. It is very important to observe its behaviour as it affects the ability to capture all the details of an object. Focus stacking is a technique in computational photography, in which a set of images focused on different planes with limited DoF are combined in order to considerably extend the DoF. Today, there is a number of focus stacking methods that can be applied in order to produce a full-focus image. In this paper, we investigate the application and effects of focus stacking on SfM-MVS 3D reconstruction. Specifically, our experiment involves the 3D reconstruction of a selected artefact using both traditional all-focus photography and focus stacking. The artefact has already been digitised with a high accuracy and resolution structured light 3D scanner, and that 3D model served as the reference model, with which SfM models were compared. We discuss on these fist results and present some preliminary assessment on the application of focus stacking for the SfM-MVS-based 3D reconstruction.},

keywords = {3D reconstruction, Focus Stacking, SfM-MVS},

pubstate = {published},

tppubtype = {article}

}

@article{Agrafiotis2017a,

title = {The effect of underwater imagery radiometry on 3D reconstruction and orthoimagery},

author = {P Agrafiotis and G I Drakonakis and A Georgopoulos and D Skarlatos},

url = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W3/25/2017/},

doi = {10.5194/isprs-archives-XLII-2-W3-25-2017},

issn = {16821750},

year  = {2017},

date = {2017-02-01},

journal = {Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch.},

volume = {42},

number = {2W3},

pages = {25--31},

abstract = {The work presented in this paper investigates the effect of the radiometry of the underwater imagery on automating the 3D reconstruction and the produced orthoimagery. Main aim is to investigate whether pre-processing of the underwater imagery improves the 3D reconstruction using automated SfM - MVS software or not. Since the processing of images either separately or in batch is a time-consuming procedure, it is critical to determine the necessity of implementing colour correction and enhancement before the SfM - MVS procedure or directly to the final orthoimage when the orthoimagery is the deliverable. Two different test sites were used to capture imagery ensuring different environmental conditions, depth and complexity. Three different image correction methods are applied: A very simple automated method using Adobe Photoshop, a developed colour correction algorithm using the CLAHE (Zuiderveld, 1994) method and an implementation of the algorithm described in Bianco et al., (2015). The produced point clouds using the initial and the corrected imagery are then being compared and evaluated.},

keywords = {SfM-MVS, Underwater 3D reconstruction, Underwater image enhancement},

pubstate = {published},

tppubtype = {article}

}