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A Flexible Method for Time-of-Flight Camera Calibration Using Random Forest

  • Chi Xu
  • Cheng Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11010)

Abstract

A learning based approach is proposed to calibrate the geometric distortion of a Time-of-Flight (ToF) camera. Our method is flexible as it requires only a ToF camera and a standard camera calibration chessboard. We treat the noise model of a ToF camera as a black box, and employ random forest to automatically learn the underlying unique noise model. The geometric property of the point-cloud can be effectively restored by the learned distortion model. The method can be used in a range of computer vision applications including e.g. hand pose estimation.

Keywords

ToF camera Calibration Random forest 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of AutomationChina University of GeosciencesWuhanChina
  2. 2.Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhanChina
  3. 3.Bioinformatics Institute, A*STARSingaporeSingapore
  4. 4.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada

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