A fast and robust extrinsic calibration for RGB-D camera networks

Po Chang Su, Ju Shen, Wanxin Xu, Sen Ching S. Cheung, Ying Luo

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. Practical applications often use sparsely-placed cameras to maximize visibility, while using as few cameras as possible to minimize cost. In general, it is challenging to calibrate sparse camera networks due to the lack of shared scene features across different camera views. In this paper, we propose a novel algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Our work has a number of novel features. First, to cope with the wide separation between different cameras, we establish view correspondences by using a spherical calibration object. We show that this approach outperforms other techniques based on planar calibration objects. Second, instead of modeling camera extrinsic calibration using rigid transformation, which is optimal only for pinhole cameras, we systematically test different view transformation functions including rigid transformation, polynomial transformation and manifold regression to determine the most robust mapping that generalizes well to unseen data. Third, we reformulate the celebrated bundle adjustment procedure to minimize the global 3D reprojection error so as to fine-tune the initial estimates. Finally, our scalable client-server architecture is computationally efficient: the calibration of a five-camera system, including data capture, can be done in minutes using only commodity PCs. Our proposed framework is compared with other state-of-the-arts systems using both quantitative measurements and visual alignment results of the merged point clouds.

Original languageEnglish
Article number235
JournalSensors (Switzerland)
Volume18
Issue number1
DOIs
StatePublished - Jan 15 2018

Bibliographical note

Funding Information:
Acknowledgments: This work was supported in part by the National Science Foundation under Grant No. 1237134.

Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • 3D reconstruction
  • Camera network calibration
  • RGB-D camera
  • Spherical object

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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