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Authors: José G. dos S. Júnior 1 ; Gustavo C. R. Lima 1 ; Adam H. M. Pinto 1 ; João Paulo S. do M. Lima 2 ; Veronica Teichrieb 1 ; Jonysberg P. Quintino 3 ; Fabio Q. B. da Silva 4 ; Andre L. M. Santos 4 and Helder Pinho 5

Affiliations: 1 Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil ; 2 Departamento de Computação, Universidade Federal Rural de Pernambuco, Recife, Brazil ; 3 Projeto de PD CIn/Samsung, Universidade Federal de Pernambuco, Recife, Brazil ; 4 Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil ; 5 SiDi, Campinas, Brazil

Keyword(s): 3D Reconstruction, Background Segmentation, Stationary Camera.

Abstract: 3D objects mapping is an important field of computer vision, being applied in games, tracking, and virtual and augmented reality applications. Several techniques implement 3D reconstruction from images obtained by mobile cameras. However, there are situations where it is not possible or convenient to move the acquisition device around the target object, such as when using laptop cameras. Moreover, some techniques do not achieve a good 3D reconstruction when capturing with a stationary camera due to movement differences between the target object and its background. This work proposes two 3D object mapping pipelines from stationary camera images based on COLMAP to solve this type of problem. For that, we modify two background segmentation techniques and motion recognition algorithms to detect foreground without manual intervention or prior knowledge of the target object. Both proposed pipelines were tested with a dataset obtained by a laptop’s simple low-resolution stationary RGB camera. The results were evaluated concerning background segmentation and 3D reconstruction of the target object. As a result, the proposed techniques achieve 3D reconstruction results superior to COLMAP, especially in environments with cluttered backgrounds. (More)

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Paper citation in several formats:
S. Júnior, J.; Lima, G.; Pinto, A.; Lima, J.; Teichrieb, V.; Quintino, J.; B. da Silva, F.; Santos, A. and Pinho, H. (2022). 3D Object Reconstruction using Stationary RGB Camera. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 793-800. DOI: 10.5220/0010807000003124

@conference{visapp22,
author={José G. dos {S. Júnior}. and Gustavo C. R. Lima. and Adam H. M. Pinto. and João Paulo S. do M. Lima. and Veronica Teichrieb. and Jonysberg P. Quintino. and Fabio Q. {B. da Silva}. and Andre L. M. Santos. and Helder Pinho.},
title={3D Object Reconstruction using Stationary RGB Camera},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={793-800},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010807000003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - 3D Object Reconstruction using Stationary RGB Camera
SN - 978-989-758-555-5
IS - 2184-4321
AU - S. Júnior, J.
AU - Lima, G.
AU - Pinto, A.
AU - Lima, J.
AU - Teichrieb, V.
AU - Quintino, J.
AU - B. da Silva, F.
AU - Santos, A.
AU - Pinho, H.
PY - 2022
SP - 793
EP - 800
DO - 10.5220/0010807000003124
PB - SciTePress

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