We propose a camera calibration method to generate a high-quality and photorealistic 3D (dimension) volumetric graphics model using several low-cost commercial RGB-D (depth) cameras located in a limited space. We show an efficient workflow to register a model efficiently and propose iterative calibration techniques to construct it. Using multiple frames, calibration in the vertical direction between the upper and lower cameras is performed. After selecting any four pairs, the calibration is performed while rotating with the vertical calibration results from other adjacent viewpoints.

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[1] Young-Ho Seo, "3D registered point cloud", IEEE Dataport, 2021. [Online]. Available: http://dx.doi.org/10.21227/901y-3102. Accessed: Dec. 11, 2023.
doi = {10.21227/901y-3102},
url = {http://dx.doi.org/10.21227/901y-3102},
author = {Young-Ho Seo },
publisher = {IEEE Dataport},
title = {3D registered point cloud},
year = {2021} }
T1 - 3D registered point cloud
AU - Young-Ho Seo
PY - 2021
PB - IEEE Dataport
UR - 10.21227/901y-3102
ER -
Young-Ho Seo. (2021). 3D registered point cloud. IEEE Dataport. http://dx.doi.org/10.21227/901y-3102
Young-Ho Seo, 2021. 3D registered point cloud. Available at: http://dx.doi.org/10.21227/901y-3102.
Young-Ho Seo. (2021). "3D registered point cloud." Web.
1. Young-Ho Seo. 3D registered point cloud [Internet]. IEEE Dataport; 2021. Available from : http://dx.doi.org/10.21227/901y-3102
Young-Ho Seo. "3D registered point cloud." doi: 10.21227/901y-3102