Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Procedural Geometry Calibration and Color Correction ToolKit for Multiple Cameras

절차적 멀티카메라 기하 및 색상 정보 보정 툴킷

  • Kang, Hoonjong (Department of Electronic Engineering, Wonkwang University) ;
  • Jo, Dongsik (School of IT Convergence, University of Ulsan)
  • Received : 2021.03.11
  • Accepted : 2021.04.03
  • Published : 2021.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, 3D reconstruction of real objects with multi-cameras has been widely used for many services such as VR/AR, motion capture, and plenoptic video generation. For accurate 3D reconstruction, geometry and color matching between multiple cameras will be needed. However, previous calibration and correction methods for geometry (internal and external parameters) and color (intensity) correction is difficult for non-majors to perform manually. In this paper, we propose a toolkit with procedural geometry calibration and color correction among cameras with different positions and types. Our toolkit consists of an easy user interface and turned out to be effective in setting up multi-cameras for reconstruction.

Keywords

Acknowledgement

This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP), grant funded by the Korean government(MSIT) (No. 2020-0-00226, Development of High-Definition, Unstructured Plenoptic video Acquisition Technology for Medium and Large Space).

References

  1. A. Soltani, H. Haibin, J. Wu, and T. Kulkarni, "Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
  2. H. Xie, H. Yao, S. Zhang, S. Zhou, and W. Sun, "Pix2Vox++: Multi-Scale Context-Aware 3D Object Reconstruction from Single and Multiple Images," International Journal of Computer Vision, no. 128. pp. 2019-2935, 2020.
  3. B. Haefner, Y. Queau, T. Mollenhoff, and D. Cremers, "Fight Ill-Posedness with Ill-Posedness: Single-Shot Variational Depth Super-Resolution from Shading," In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
  4. Y. Lin, V. Larsson, M. Geppert, Z. Kukelova, M. Pollefeys, and T. Sattler, "Infrastructure-Based Multi-Camera Calibration Using Radial Projections," In European Conference on Computer Vision(ECCV), pp. 327-344, 2020.
  5. S. C. Lim and D. Y. Kim, "A Calibration Method for Multimodal Dual Camera Environment," Journal of the Korea Institute of Information and Communication Engineering, vol. 19, no. 9, pp. 2138-2144, 2015. https://doi.org/10.6109/jkiice.2015.19.9.2138
  6. K. Li, Q. Dai, and W. Xu, "Collaborative Color Calibration for Multi-Camera Systems," Signal Processing: Image Communication, vol. 26, no. 1, pp. 48-60, 2011. https://doi.org/10.1016/j.image.2010.11.004
  7. R. Usamentiaga and D. F. Garcia, "Multi-Camera Calibration for Accurate Geometric Measurements in Industrial Environments," Measurement, vol. 134, pp. 345-358, 2019. https://doi.org/10.1016/j.measurement.2018.10.087