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Article

Surface Reconstruction Assessment in Photogrammetric Applications

1
LIS UMR 7020, Aix-Marseille Université, CNRS, ENSAM, Université De Toulon, Domaine Universitaire de Saint-Jérôme, Bâtiment Polytech, Avenue Escadrille Normandie-Niemen, 13397 Marseille, France
2
3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), 38123 Trento, Italy
3
Laboratory of Photogrammetry, National Technical University of Athens (NTUA), 15780 Athens, Greece
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(20), 5863; https://doi.org/10.3390/s20205863
Received: 10 September 2020 / Revised: 7 October 2020 / Accepted: 12 October 2020 / Published: 16 October 2020
The image-based 3D reconstruction pipeline aims to generate complete digital representations of the recorded scene, often in the form of 3D surfaces. These surfaces or mesh models are required to be highly detailed as well as accurate enough, especially for metric applications. Surface generation can be considered as a problem integrated in the complete 3D reconstruction workflow and thus visibility information (pixel similarity and image orientation) is leveraged in the meshing procedure contributing to an optimal photo-consistent mesh. Other methods tackle the problem as an independent and subsequent step, generating a mesh model starting from a dense 3D point cloud or even using depth maps, discarding input image information. Out of the vast number of approaches for 3D surface generation, in this study, we considered three state of the art methods. Experiments were performed on benchmark and proprietary datasets of varying nature, scale, shape, image resolution and network designs. Several evaluation metrics were introduced and considered to present qualitative and quantitative assessment of the results. View Full-Text
Keywords: surface reconstruction; mesh model; 3D reconstruction; visibility constraints; volumetric methods; dense point cloud; multiple view stereo (MVS); dense image matching (DIM); photogrammetry; computer vision surface reconstruction; mesh model; 3D reconstruction; visibility constraints; volumetric methods; dense point cloud; multiple view stereo (MVS); dense image matching (DIM); photogrammetry; computer vision
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MDPI and ACS Style

Nocerino, E.; Stathopoulou, E.K.; Rigon, S.; Remondino, F. Surface Reconstruction Assessment in Photogrammetric Applications. Sensors 2020, 20, 5863. https://doi.org/10.3390/s20205863

AMA Style

Nocerino E, Stathopoulou EK, Rigon S, Remondino F. Surface Reconstruction Assessment in Photogrammetric Applications. Sensors. 2020; 20(20):5863. https://doi.org/10.3390/s20205863

Chicago/Turabian Style

Nocerino, Erica, Elisavet K. Stathopoulou, Simone Rigon, and Fabio Remondino. 2020. "Surface Reconstruction Assessment in Photogrammetric Applications" Sensors 20, no. 20: 5863. https://doi.org/10.3390/s20205863

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