3D Object Reconstruction using Stationary RGB Camera

José G. dos S. Júnior, Gustavo C. R. Lima, Adam H. M. Pinto, João Paulo S. do M. Lima, Veronica Teichrieb, Jonysberg P. Quintino, Fabio Q. B. da Silva, Andre L. M. Santos, Helder Pinho

2022

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.

Paper Citation

in Harvard Style

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, SciTePress, pages 793-800. DOI: 10.5220/0010807000003124

in Bibtex Style

@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},
}

in EndNote Style

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
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