Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Generic Camera Calibration using Blurry Images

Zezhun Shi

TL;DR

This work draws on geometric constraints and a local parametric illumination model to simultaneously estimate feature locations and spatially varying point spread functions, while re solving the translational ambiguity that need not be considered in con ventional image deblurring tasks.

Abstract

Camera calibration is the foundation of 3D vision. Generic camera calibration can yield more accurate results than parametric cam era calibration. However, calibrating a generic camera model using printed calibration boards requires far more images than parametric calibration, making motion blur practically unavoidable for individual users. As a f irst attempt to address this problem, we draw on geometric constraints and a local parametric illumination model to simultaneously estimate feature locations and spatially varying point spread functions, while re solving the translational ambiguity that need not be considered in con ventional image deblurring tasks. Experimental results validate the effectiveness of our approach.

Generic Camera Calibration using Blurry Images

TL;DR

This work draws on geometric constraints and a local parametric illumination model to simultaneously estimate feature locations and spatially varying point spread functions, while re solving the translational ambiguity that need not be considered in con ventional image deblurring tasks.

Abstract

Camera calibration is the foundation of 3D vision. Generic camera calibration can yield more accurate results than parametric cam era calibration. However, calibrating a generic camera model using printed calibration boards requires far more images than parametric calibration, making motion blur practically unavoidable for individual users. As a f irst attempt to address this problem, we draw on geometric constraints and a local parametric illumination model to simultaneously estimate feature locations and spatially varying point spread functions, while re solving the translational ambiguity that need not be considered in con ventional image deblurring tasks. Experimental results validate the effectiveness of our approach.
Paper Structure (42 sections, 27 equations, 9 figures, 1 table)

This paper contains 42 sections, 27 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminary
  4. Brown Conrady model
  5. Homography and Camera Motion
  6. Translational Ambiguity in Deconvolution
  7. Method
  8. Overview
  9. Local Deconvolution
  10. Local Alignment
  11. Global Alignment
  12. Spatially varying bias compensation.
  13. Experiments
  14. Patterns Comparison
  15. Global Alignment Verification
  16. ...and 27 more sections

Figures (9)

  • Figure 1: Parametric model and generic model. The latter can yield more accurate results than former but requires far more calibration images.
  • Figure 2: Generic camera calibration requires far more images than parametric calibration, making motion blur practically unavoidable. Standard deconvolution recovers visually plausible images but leaves feature positions ambiguous up to an arbitrary translation due to the shift equivariance of convolution.
  • Figure 3: Overview of the proposed method. We use local homographies and illumination parameters to replace classical feature based global mapping to describe the latent image, with geometric constraints enforcing inter-block consistency. Global translational ambiguity is resolved by alignment with a calibrated parametric camera.
  • Figure 4: Deconvolution results for four PSF glyphs under checkerboard and star calibration patterns, with and without 5% Gaussian noise. Each group shows an $80\times80$ and $160\times160$ px crop with SSIM/PSNR. The star pattern is significantly more robust to noise.
  • Figure 5: Distribution of reprojection direction (hue) and magnitude.
  • ...and 4 more figures